Pay and flexible working top the list of things that experienced hires are after – and if you can’t offer both, then you’ll need to consider lowering your expectations in a competitive market.

The Big Two: pay and flexibility 

Job seekers in tech have spoken: the most important priorities for tech candidates are compensation (yes, pay), followed by flexible working arrangements. 

The ‘Big Two’ factors are also ranked the fastest-growing priorities year over year, according to LinkedIn’s recent research, The Future of Recruiting 2023.

So, the thorny issue of pay – the one thing you were never supposed to mention at an interview – is now the key thing people are looking to know upfront. That, followed by the expectation that they can be fully remote if they want to be.

Why are employers reluctant to talk about pay?

Very few employers like to get out there and say “we pay great salaries.” Of course, everyone thinks that they offer above the market average, but few lead with it. Why? 

Because generally, most businesses don’t want to hire people who they perceive are motivated solely by money, because, in their mind, they’re harder to keep happy. And that’s why traditionally, job ads follow the same predictable structure: company size, clients, tech stack, and a touch of benefits (progression plans and training). But pay? That’s usually left until the second interview, by which both sides may be wasting their time.

Changing priorities, challenging times

So why are pay and flexible working driving the market? Two reasons.
On the pay front – it’s pretty obvious. We’ve got a cost of living crisis. Rising inflation, stagnating real wages. Job seekers literally can’t afford to be coy about what they can expect from their wage packet.

Flexible working, on the other hand, is a hangover from the pandemic. Hires, especially experienced ones, have grown used to a new way of working and they’re unwilling to go back, certainly not in the way they were used to.

Why it matters to employers

Frankly, if you’re not offering the ‘big two’ as an employer, you’re not just slightly behind, you’re way behind – to the point where you might not even be shown CVs for experienced hires. And that’s an issue, when you’re trying to recruit and keep people. It’s an issue for all sectors, of course, but it’s particularly prevalent in tech because the demand for skills is so high. 

In tech, hires can afford to be picky

While some companies are forcing people back to the office, in tech, employees can afford to be picky. In a sector where people are being approached once, twice a week for their skills: there’s always someone, somewhere who can offer better money and better flexibility. If you’ve got a loyal tech employee, then you’ve done something right; they’re working with you because they want to be there. 

Can’t offer more? There is an alternative.

We get that not all companies are in a position to offer high or better salaries, and not all companies are able, or willing, to offer flexible working. Assuming you don’t want to go offshore, there’s one way around that. 

Hire people who are less experienced and/or more junior than you would’ve considered. 

This can work, and here’s why: junior candidates are more likely to want to come into the office. They’re less likely to have family duties, which are a real benefit to home workers. Going into the office four days a week doesn’t require major adjustments in their personal lives to accommodate. Of course, juniors will still look to their peers and see flexible working happening there and would likely expect at least one day from home, so you’ll need to factor this into your offer, too.  

Want great hires? Think pay & flexibility first

In a nutshell: if you want experienced candidates, then a good salary and significant flexibility in working hours are an absolute must. If you haven’t, then by definition, you’re automatically shopping in a junior market.

In today’s data-driven world, companies are increasingly investing in data engineering to power analytics, machine learning, customer insights, and automated decision-making. Data engineers are the professionals who build and maintain the pipelines that move data from raw sources into meaningful, usable formats. As organisations scale their data efforts, the skills they look for in data engineering talent are evolving rapidly. For hiring teams and talent strategists, understanding the most relevant capabilities in data engineering not only helps create better job descriptions but also supports smarter resourcing decisions.

1. Programming and Query Languages

At the heart of data engineering is the ability to work with data programmatically. SQL remains a foundational language for querying, transforming, and analysing data from relational databases, and it continues to show up in the vast majority of job postings for data engineers. In addition to SQL, Python has become one of the most widely used languages for data engineering tasks because of its versatility, extensive ecosystem of libraries and its suitability for scripting and automation.

“At a high level, you should expect proficiency in SQL and Python, big data tools (Apache Spark, Hadoop, Hive), ETL & data pipeline (Airflow), and databases.” – DoIt Software

Mastery of Python and SQL enables engineers to write clean, efficient data pipelines and serve as a base for more advanced capabilities like automation and integration with workflow tools. This combination of programming and database language skills consistently ranks high in industry skill reports as essential for data engineering roles.

2. Data Pipeline Design and Orchestration

A key part of the data engineer’s job is building systems that reliably move and transform data. This includes designing pipelines that extract data from one system, transform it into the needed format, and load it into target systems such as data warehouses or analytics platforms. Knowing how to design robust ETL (extract, transform, load) or ELT (extract, load, transform) workflows is critical.

“However, getting from raw, scattered information to high-quality, usable datasets takes a robust data infrastructure, along with skilled professionals who can design, build, and maintain it.” – Data Engineering Jobs

Equally important is familiarity with orchestration tools such as Apache Airflow, Prefect, or Dagster that automate and manage complex workflows, ensuring that each step in a pipeline runs in the correct order, handles errors gracefully, and scales as data grows. Organisations increasingly expect data engineers to be comfortable with these orchestration technologies as part of building resilient, automated data infrastructure.

3. Cloud Platform Expertise

Most modern data architectures are cloud-centric, moving away from traditional on-premise systems to services provided by hyperscalers like AWS, Google Cloud Platform or Microsoft Azure. Cloud platforms offer managed services for storage, compute and analytics, such as data lakes, serverless compute and distributed processing engines.

Data engineers must understand how to design and optimise pipelines using cloud-native services, configure security controls, and manage costs effectively. In many job descriptions, proficiency with at least one major cloud provider is no longer optional, it is a core requirement, reflecting the widespread shift toward cloud-based data infrastructure in enterprise environments.

“Over 94% of enterprises have embraced cloud technologies. If you’re not fluent in at least one major cloud platform, you’re essentially unemployable as a data engineer in 2026.” – Medium

4. Big Data and Real-Time Processing

As the volume and velocity of data increase, organisations move beyond simple batch processing to architectures that handle real-time or near-real-time data streams.

Technologies like Apache Spark for distributed processing and Apache Kafka or Flink for streaming data have become central to modern data engineering. Engineers who can build systems that process both historical and streaming data in scalable ways are highly desirable, especially for businesses that rely on real-time analytics for user personalisation, fraud detection, operational alerts, or dynamic reporting.

Mastery of big data frameworks and stream processing is increasingly seen as a differentiator in the hiring market.

5. Data Modeling, Governance and Quality

Beyond moving and processing data, data engineers are responsible for making sure that data is structured in a way that downstream users can trust and understand. This includes tasks such as designing schemas and data models that support analytical queries efficiently, implementing governance practices that ensure data integrity and compliance, and building systems that monitor data quality. Good governance and quality practices help organisations avoid costly errors and ensure that analytics and machine learning models are built on reliable foundations. As companies grapple with increased regulatory pressure and a broader need for data transparency, experience in these areas is emerging as a key hiring criterion.

Hiring Implications

From a hiring perspective, these five skills form a strong foundation for building robust and scalable data systems. For resourcing teams, this translates into a greater emphasis on core technical fundamentals, particularly assessing candidates’ proficiency in SQL and Python, as these languages underpin most data engineering work.

It also means placing real weight on experience with pipeline automation and orchestration, since this reflects a candidate’s ability to manage real-world operational complexity rather than isolated technical tasks. Job descriptions increasingly need to prioritise cloud platform fluency, ensuring candidates can operate effectively within the environments the organisation already relies on.

At the same time, hiring teams should recognise the growing importance of big data processing and streaming capabilities, which are closely linked to high-impact use cases such as real-time analytics and operational insight.

Finally, embedding data modelling, governance and quality considerations into interview criteria helps identify engineers who can translate technical expertise into reliable, compliant and business-ready data assets.

Ultimately, the strongest data engineering hires combine deep technical skill with a clear understanding of how their work supports business outcomes. Organisations that align their resourcing strategies with these evolving skill demands are better positioned to attract and retain the talent needed to enable data-driven decision-making and reduce the risk of data initiatives failing due to capability gaps.

As organisations continue to embrace remote work, hybrid applications, and digital-first operations, the old model of usernames and passwords as the primary way to verify identity is fading fast. Identity is no longer just a login check; it’s a critical control plane for both security and user experience. With cyber threats becoming more sophisticated and digital ecosystems sprawling across cloud, mobile and IoT environments, companies are shifting toward models like passwordless authentication, decentralised identity, and Identity-as-a-Service (IDaaS), and this shift has implications for how teams are resourced and hired.

The Rise of Passwordless Authentication

Modern identity strategies are increasingly moving away from traditional passwords toward more secure, phishing-resistant methods such as passkeys, biometrics, and cryptographic credentials. Instead of relying on something the user remembers, systems are tying authentication to something the user has or is, such as a device-based key or biometric factor. These changes reduce the risk of credential theft and eliminate a huge source of helpdesk tickets, improving user experience while tightening security. Passwordless authentication, driven by standards like FIDO2 and WebAuthn, is no longer experimental. Leading enterprises are adopting it as a default for workforce and customer access.

“By 2026, passwordless authentication will move from early adoption to default enterprise posture for many use cases.” – IT Security Pundit

For hiring teams, this evolution means that identity engineering and authentication expertise are becoming core competencies rather than niche technical skills. Recruiters and talent leaders need to look beyond traditional IAM roles to find candidates who understand how to implement passwordless frameworks, integrate them with existing systems, and ensure a seamless user experience without compromising security.

Identity-as-a-Service and Cloud-Native IAM

Identity-as-a-Service, or cloud-based IAM platforms, have become mainstream as organisations seek scalability and simplified management of access controls across diverse environments. IDaaS solutions offer integrated single sign-on (SSO), multi-factor authentication (MFA), lifecycle management, and compliance auditing – all delivered from the cloud. In a world of hybrid work and SaaS sprawl, IDaaS helps centralise identity control even when applications and users are distributed.

From a resourcing perspective, this trend underscores the need for skills in cloud IAM architectures and service integration. Teams must be capable of selecting, implementing, and optimising cloud identity platforms that align with broader security strategies, while also orchestrating access across legacy systems, partner ecosystems, and third-party applications.

Decentralised Identity and Verifiable Credentials

Looking beyond centralised directories, decentralised identity (DID) and verifiable credentials are gaining traction as ways to give individuals and systems more control over their digital identities. Instead of storing identity data in a central repository, decentralised identity frameworks allow users to hold credentials in secure wallets and share only the information required for specific interactions. This approach reduces enterprise liability and enhances privacy, making it attractive in regulated environments or cross-organisational contexts.

“This adoption of phishing-resistant authentication marks a paradigm shift in cybersecurity, with FIDO passkeys and hardware keys poised to become the gold standard in authentication by 2027.” – Hypr

For hiring teams, decentralised identity represents a new frontier in identity engineering. Building and maintaining systems that can issue, verify, and govern verifiable credentials (and integrating these into enterprise IAM stacks) requires hybrid expertise that combines cryptography, standards like W3C’s DID, and real-world integration experience. Talent with experience in cutting-edge identity standards will be increasingly in demand as organisations explore pilot projects and phased rollouts.

IAM as the Control Plane for Security and UX

Today’s identity platforms are more than authentication checkboxes. They are security control planes that enforce access policies, drive zero-trust strategies, and shape user experience across cloud, on-premise and hybrid applications. IAM now intersects with adaptive access, identity governance, and lifecycle automation, making it central to both security outcomes and operational efficiency.

“The rapid adoption of cloud services, Software-as-a-Service (SaaS) platforms, remote work, Internet of Things (IoT), and artificial intelligence has dramatically expanded the attack surface. Cybercriminals are no longer focused solely on exploiting network vulnerabilities; instead, they target identities—user credentials, access privileges, and authentication systems. As a result, IAM has evolved from a back-office IT function into a strategic business priority.” – SIIT

This expanded role means organisations must rethink how they resource IAM. Traditional “helpdesk” or “directory admin” tasks are no longer sufficient. Instead, companies need identity architects, IAM product owners, security engineers with identity focus, and change leaders who can guide cross-functional teams through complex integrations and migrations. These roles require both technical depth and an understanding of how identity impacts compliance, user productivity, and business risk.

What This Means for Hiring and Talent Strategy

As identity moves to the centre of both user experience and security architecture, resourcing strategies must evolve accordingly. Hiring managers should look for professionals who can speak fluently about passwordless strategies, cloud IAM platforms, decentralised identity standards and identity governance. Organisations that treat identity as a foundational platform rather than an afterthought are better positioned to support modern digital workforces and protect against emerging threats.

In practical terms, this could mean adjusting job descriptions to emphasise cross-domain skills, investing in upskilling existing teams, or partnering with talent pipelines that focus on security and cloud engineering. It may also require aligning hiring priorities with security and IT leadership to ensure that identity roles are not siloed, but integrated with broader enterprise security and digital transformation initiatives.

Secure identity and authorisation are no longer optional enhancements, they are essential enablers of business continuity and growth in a world where people, devices, and applications interact in complex ways. Organisations that recognise this early and resource appropriately will be better prepared to protect their systems while delivering seamless experiences for users.

Digital transformation has been a top strategic priority for organisations across sectors, with leaders betting on technology to drive growth, efficiency, and competitive advantage. Yet the pathway from ambition to impact is littered with projects that stall, lose momentum, or never achieve their intended outcomes. According to multiple industry analyses, a large proportion of digital transformation initiatives fail to deliver their expected value, often not because of technology, but due to deeper organisational and execution issues.

For hiring and resourcing teams, recognising the early warning signs of a faltering transformation is essential. These signals usually show up well before a project collapses, and they have clear implications for workforce planning, skill prioritisation, and talent strategy.

1. Business Value Doesn’t Materialise

One of the first indicators that a transformation is off track isn’t technical at all, it’s practical impact. If new systems or processes are live but the organisation isn’t seeing measurable improvements in productivity, customer experience, or competitive advantage, the initiative may be drifting without delivering value. Organisations often describe this as “doing digital for its own sake” where teams follow activity without clear outcomes.

“This indicator, he notes, often reveals itself as a growing cynicism within the organization, with teams feeling like they’re simply “doing digital” for its own sake without a clear understanding of the “why” or seeing any real positive impact.” – CIO

When this happens, hiring teams should question whether they are resourcing the right capabilities. It’s a sign that project roles focused solely on delivery milestones may need to be complemented by roles that bridge technology and business outcomes, such as product strategists, value realisation leads, or business analysts with change expertise.

2. Goals Are Misaligned Across the Organisation

If IT and business units aren’t aligned on what success looks like, progress can stall. Misalignment often shows up as low adoption of new tools, teams reverting to old processes, or complaints that solutions don’t meet real needs. This disconnect typically stems from weak engagement during planning and execution phases, and a failure to involve end users in shaping requirements.

From a resourcing perspective, this is a sign to evaluate not just the number of people, but the mix of skills. Projects with strong business engagement are often staffed with transformation leads who are comfortable facilitating cross-functional alignment, not just managing deliverables.

3. Leadership Support and Governance Are Weak

Transformations that lose executive sponsorship or governance control tend to drift. Without clear accountability mechanisms and a governance structure that keeps strategic priorities visible, teams can lose focus and progress slows.

Recruiters and talent strategists should watch for this in job briefs and leadership reporting lines. Organisations that lack strong sponsorship may also lack clear role definitions for transformation leadership, making it hard to attract senior change and technology leaders who can drive accountability.

4. Technical Foundation Is Inadequate

Outdated infrastructure and legacy systems remain a fundamental barrier to successful transformation. Recent reporting highlights that many large organisations continue to depend on ageing technology, creating significant integration issues and operational friction.

“Around two-thirds (63%) of respondents said they rely on anywhere between one and 10 legacy applications in both the front and back end every day. More than one-quarter (29%) also depend on anywhere up to 20 legacy applications. This the acute challenges faced when moving to more modern tech stacks, given many business-critical applications rely on these outdated systems.” – ITpro

For hiring teams, this signals the need to prioritise modernisation and integration expertise. Candidates with experience in migrating legacy platforms, implementing scalable architectures, and managing technical debt are critical in pulling struggling transformations back on course.

5. Teams Are Experiencing Fatigue and Resistance

Even large organisations that remain committed to transformation can hit a human barrier: fatigue and resistance. Surveys of IT and delivery teams show that constant change without clear wins can lead to burnout, reduced morale, and increased attrition.

From a resourcing angle, this isn’t just a volume problem. It’s a quality and support problem. When transformation fatigue rises, organisations need talent with strong change leadership, internal communication skills, and the ability to design humane transformation rhythms that preserve workforce health and engagement.

“50% of respondents reported experiencing “transformation fatigue” due to lengthy overhaul processes and tight deadlines.. Meanwhile, 44% said the frequency of change is too high and adds more pressure. All told, 45% said they’ve suffered from burnout as a result of ongoing internal changes, while 36% said they would consider quitting due to constant upheaval.” – ITpro

6. Strategy Lacks a Clear Roadmap or Metrics

Finally, a project that lacks a clear strategic roadmap with measurable milestones is at risk of drifting. Ambiguous success criteria mean teams cannot assess progress realistically, and stakeholders lose confidence in the journey.

For talent teams, this is a cue to invest in governance, planning, and data capability. Roles that can define, measure, and course-correct based on solid data (such as transformation performance analysts or portfolio management specialists) help anchor initiatives to real business goals.

What This Means for Hiring and Resourcing

Recognising these warning signs early gives hiring leaders an opportunity to adapt resourcing approaches before a transformation stalls completely. Traditional hiring models that focus purely on delivery skills may not suffice for projects that are already off course. Instead, organisations should consider:

Updating role profiles to prioritise outcome ownership and business value realisation rather than just technical delivery.

Strengthening governance and leadership by recruiting transformation executives and sponsors who can drive strategic alignment.

Injecting specialised expertise into teams, such as integration architects, change managers, and workforce engagement specialists, to address the root causes of stalling initiatives.

Finally, monitoring workforce sentiment, workload, and capability gaps as early indicators of risk can be as valuable as tracking project milestones.

Digital transformation is inherently complex, and many fail not because technology doesn’t work, but because organisations don’t adapt their people, processes, and prioritisation to the scale of change required. Understanding and acting on the warning signs above helps hiring teams anticipate talent needs, adjust strategies, and support transformations that deliver real business impact — not just technical output.

The software talent market remains one of the most dynamic parts of the global economy as organisations shift from traditional IT delivery toward continuous digital innovation. Companies increasingly depend on software professionals to build and evolve products, integrate AI capabilities, modernise cloud environments and secure critical systems.

For hiring and resourcing teams, understanding how this market is changing (and which roles are actually driving digital growth) is essential to attracting and retaining top talent.

Evolving Demand for Software Engineers

Software engineering remains foundational to digital growth. Even as artificial intelligence reshapes how code is written and workflow tools automate routine tasks, organisations still need engineers who can build, architect and maintain robust applications and platforms.

In 2025, studies showed that demand for software developers and engineers was growing across industries including finance, healthcare, industrial automation and technology services, reflecting how widespread software adoption has become.

“Hiring in software engineering is no longer exclusive to traditional tech companies. The past few months have shown strong demand from unexpected sectors, while some industries are pulling back.” – Aura

Organisations are particularly seeking professionals with Python, JavaScript, SQL and cloud platform experience, highlighting the importance of both general programming skills and specialised technical knowledge.

Senior software engineers are increasingly prioritised over entry-level hires as businesses seek professionals who can solve complex problems and guide technical decisions. There is also a noticeable shift toward roles that bridge development with system design, architecture, and cross-functional collaboration.

This means recruiters should highlight experience with scalable systems, cloud native solutions and AI-augmented development environments in job descriptions to attract candidates who add long-term strategic value.

Cloud and Full-Stack Engineers Propel Growth

Modern organisations are embracing hybrid and cloud-native architectures as part of their digital transformation strategies. This has pushed demand for engineers who can work across the full software stack, from front-end interfaces to back-end services and distributed systems. Full-stack engineers, who can fluidly navigate multiple technologies and frameworks, are particularly valuable in smaller or mid-sized companies where lean teams need versatility and adaptability. Companies were actively advertising full-stack roles with a strong emphasis on cloud experience and remote collaboration capabilities, illustrating the broad need for multi-skilled talent.

Cloud expertise continues to be a critical driver of software hiring. Organisations that migrate legacy systems to cloud platforms like AWS, Azure or Google Cloud need engineers who not only understand these environments but can architect solutions that maximise security, performance and business value. Recruiters must prioritise candidates with demonstrable cloud project experience, infrastructure-as-code proficiency, and familiarity with containerisation and orchestration tools – skills that are signals of readiness to support digital growth.

AI and Machine Learning Roles Reshape Recruitment

Artificial intelligence is influencing the software talent market in multiple ways. On one hand, tooling such as AI-powered code generation and automation platforms increases developer productivity and changes daily job practices. On the other hand, it has created new, specialised roles focused on integrating AI into products and workflows. Organisations are hiring professionals who can design machine learning models, integrate generative AI capabilities, optimise AI-driven features, and ensure quality controls around automated systems. These roles often require a blend of software engineering expertise and data science fundamentals.

For hiring teams, this means adjusting job criteria to include experience with frameworks like TensorFlow, PyTorch, and real-world deployment of AI systems. Even when talent markets fluctuate, demand for AI skills continues to outpace many traditional software specialisations, making these candidates a strategic priority.

“Demand for AI engineers has increased by 25% over the past three years. Pnet says it is one of the most future-oriented career paths in the information technology sector.” – MyBroadband

Strategic Software Roles Beyond Code

In addition to traditional development positions, strategic software roles are helping organisations capture value from digital initiatives. Forward-deployed engineers, for example, combine deep technical expertise with client-facing responsibilities, helping customise and optimise complex software systems for specific business needs. This role has seen explosive growth as companies seek to embed engineering talent closer to product delivery and adoption contexts.

Another emerging area is the integration of security and quality engineering into development processes. With cyber threats escalating and compliance requirements tightening, software teams are increasingly hiring engineers with security-centric skill sets such as secure coding, threat modelling and automated testing pipelines. These professionals help ensure that growth doesn’t come at the expense of risk exposure, making them highly attractive in modern talent markets.

What This Means for Hiring and Resourcing

For hiring managers and talent strategists, the key takeaway from the current software talent landscape is that roles driving digital growth are both technical and strategic. Organisations seeking to scale digital products need to prioritise candidates with flexible skill sets: those who can build code, understand cloud ecosystems, integrate AI, and work across teams.

“Senior professionals are also more likely to be offered remote or hybrid positions, with companies showing greater trust in their ability to work independently.” – Mev

Resourcing strategies should evolve to attract senior engineers who can mentor teams and navigate ambiguous problems, as well as specialised professionals comfortable with emerging tools and platforms. Job descriptions should emphasise not just technical requirements but the organisation’s vision for innovation, opportunities for growth, and the impact the role will have on digital transformation outcomes.

Organisations that align their hiring approach with the realities of the software talent market by investing in people who can shape digital experiences, secure systems, and accelerate delivery, will be best positioned to drive sustainable growth in a fast-changing technology landscape.

When a fast-growing software company plants its global headquarters in a region, it does more than move desks. It changes hiring patterns, reshapes salary expectations, attracts new suppliers, and signals to investors that something serious is happening.

That is why Halo moving its global HQ into the Willis Building in Ipswich matters. It is not just a property story. It is a talent story. And from a hiring and resourcing perspective, it could mark the moment Suffolk’s SaaS ecosystem shifts from promising to industrialised.

Anchor Companies Change Labour Markets

Every major tech cluster starts with one or two anchor companies. These firms do not just create jobs. They create capability. They train people, spin out founders, attract recruiters, bring in investors, and raise the bar for what “good” looks like in that region.

Look at what happened in Stockholm. The success of Spotify did not just create roles inside one organisation. It helped turn Stockholm into one of Europe’s highest-producing tech hubs per capita. An overview from Stockholm Business Region highlights how the city continues to produce globally recognised SaaS and fintech scale-ups.

Spotify alumni went on to build or fund other ventures. Recruiters built specialist tech desks. Universities aligned courses to industry demand. Venture capital followed proven execution. The ecosystem industrialised because one breakout SaaS company proved it could be done.

Suffolk Has the Same Ingredients

Suffolk is not Stockholm. But ecosystems do not start as ecosystems. They start as outliers.

Halo’s decision to place its HQ in Ipswich sends a hiring signal. It tells engineers, product managers and commercial SaaS leaders that they can build a serious career without relocating to London, Cambridge or overseas. It tells investors there is execution capability locally. And it tells graduates that high-growth tech does not have to be something you watch from afar.

Regional growth patterns back this up. A report from Tech Nation shows that UK tech growth continues to decentralise beyond London, with regional hubs driving a larger share of scale-up activity than in previous years.

From a hiring standpoint, decentralisation changes everything. When a single high-performing SaaS business embeds itself in a region, it raises salary benchmarks, improves candidate quality, and encourages previously remote talent to consider local opportunities.

Industrialising Talent Means Repeatable Hiring Pipelines

Industrialisation sounds like a manufacturing term, but in SaaS hiring it means something simple: repeatability.

It means graduate pipelines feeding into structured onboarding. It means mid-level engineers trained into senior roles locally rather than imported. It means commercial leaders who understand subscription metrics, churn dynamics and ARR growth.

In Tel Aviv, anchor companies such as Wix helped normalise SaaS skillsets at scale. Today the region is globally recognised for its density of product and cybersecurity talent. Startup Nation Central notes in its 2026 ecosystem data that Israel maintains one of the highest concentrations of startups per capita globally.

“Startup Nation Central continues to showcase this remarkable ecosystem, where Israel ranks #1 in startups per capita, #1 in public companies on Nasdaq, #4 in R&D investment as a percentage of GDP, and #2 in innovation linkages.” – Startup Nation Central

That density did not appear by accident. It came from repeat hiring, spin-outs, and knowledge transfer.

If Halo continues to scale from Suffolk, it can create similar multiplier effects. Employees trained inside a high-growth SaaS environment often go on to found new ventures, join other local firms, or mentor emerging teams. Recruiters and resourcing partners then build specialisms around that sector. Universities adapt. Apprenticeships align.

This is how regions move from isolated tech employers to recognisable SaaS clusters.

The Resourcing Impact: Competition, Standards and Spin-Outs

From a hiring perspective, anchor HQ moves do three things almost immediately.

They increase competition for top talent. That pushes local businesses to sharpen their employer value proposition. It can feel uncomfortable at first, but it raises standards across the board.

They professionalise recruitment. As headcount scales, structured hiring replaces ad-hoc recruitment. Talent acquisition becomes data-driven. Workforce planning becomes proactive.

And they create future founders. Former employees take experience into new ventures. That is exactly what happened in Dublin after global SaaS and fintech players scaled operations there. IDA Ireland has repeatedly highlighted how multinational tech anchors strengthened the indigenous startup ecosystem by seeding experienced talent into the local market.

If Halo scales significantly from Ipswich, Suffolk could see a similar pattern over the next five to ten years.

Accountability and Execution Still Matter

None of this happens automatically. Anchor companies industrialise regions only if they continue to grow and invest locally.

That means building structured graduate schemes. Partnering with local colleges. Investing in leadership development. Supporting meetups and industry forums. Encouraging knowledge-sharing rather than operating in isolation.

It also means hiring with intent. If Halo builds a dense concentration of SaaS product, engineering and revenue leadership in Suffolk, it creates transferable capability. If hiring remains fragmented or overly remote, the multiplier effect weakens.

Stockholm’s growth was not just about Spotify existing. It was about Spotify embedding itself into the city’s ecosystem.

Why This Moment Matters for Suffolk

From a resourcing standpoint, Halo’s HQ move could represent a turning point.

It reduces the psychological barrier that serious SaaS careers require relocation. It improves regional credibility with investors and candidates. It encourages experienced professionals who moved away to reconsider returning. And it signals that Suffolk is not just a place to live – it is a place to build.

Industrialisation of talent is not glamorous. It is slow, structured and built on hiring discipline. But once it begins, it compounds.

If Halo continues to scale and embed in Ipswich, Suffolk could shift from being a location with tech companies to becoming a recognisable SaaS hub in its own right.

And in hiring terms, that changes everything.

Demand for Business Analysts in the IT field continues to grow as organisations work to transform digitally, improve customer experiences and extract value from data.

Hiring teams and talent strategists are increasingly focused on securing candidates who not only understand business needs but can translate them into workable technology solutions. In a competitive market for skilled analysts, knowing which capabilities truly matter helps hiring managers craft better job descriptions, assess talent more effectively, and build teams that can deliver meaningful outcomes.

1. Strong Requirements Gathering and Documentation

The ability to understand, elicit, and document requirements remains the foundational skill for Business Analysts in IT. At its core, this skill enables analysts to bridge the gap between business stakeholders and technical teams. An analysis of BA demand highlights that organisations increasingly value candidates who can ask insightful questions, unpack complex processes, and record detailed, clear requirements that development teams can act on. This ensures that IT solutions align with business needs and reduces costly rework later in delivery.

“A business analyst bridges the gap between business needs and technological solutions. With the ability to analyze data, identify inefficiencies, and recommend actionable improvements, BAs have become essential in driving profitability and performance.” – H2K Infosys

2. Data Literacy and Analytical Thinking

Business Analysts in IT are expected to work with data as much as with stakeholders. Today’s analysts need to interpret data, generate insights, and validate assumptions using quantitative evidence. This means knowing how to query data sources, interpret dashboards and use data to inform recommendations. A report on analytics and business roles confirms that data literacy is a top skill employers seek in BAs, reflecting how data-driven decision making has moved from an advantage to a necessity.

3. Technical Fluency

Although Business Analysts are not typically required to be software developers, they must be technically fluent enough to understand architecture, development constraints and integration challenges. This includes familiarity with APIs, cloud platforms, DevOps practices, and the software development lifecycle.

IT leaders increasingly emphasise this skill because it allows BAs to communicate effectively with engineers, anticipate technical trade-offs, and help ensure that solutions are not only desirable but feasible and scalable. In industry commentary, employers note that BAs who can “speak tech” reduce friction between business and delivery teams and accelerate project timelines.

4. Change Management and Stakeholder Engagement

Business Analysts frequently act as the connective tissue across departments. They manage expectations, navigate conflicting priorities and help business users adapt to new processes or systems. For IT transformations to succeed, BAs must be skilled at stakeholder engagement, influence without authority, and facilitating collaboration.

Organisations with strong stakeholder alignment and engagement, often led by Bas, were much more likely to achieve project success. This makes change management skills particularly valuable because they ensure adoption and value realisation beyond delivery.

“Better communication, as a result of good stakeholder engagement, leads to smoother project execution, fewer conflicts, and a higher chance of achieving project goals.” – Tigernix

5. Agile and Adaptive Delivery Mindset

The way software is delivered has shifted dramatically over the last decade, with agile methodologies now the dominant delivery model in IT. Business Analysts are expected to understand agile frameworks like Scrum and Kanban, contribute to backlog refinement, and support iterative delivery cycles.

More than ever, organisations value BAs who can adapt to changing priorities, break down work into incremental value slices, and work closely with product owners and delivery teams to ensure outcomes are continuously delivered and validated. In 2026, agile fluency, including facilitation of agile ceremonies and iterative requirement refinement, is routinely listed in IT BA job descriptions.

What This Means for Hiring and Resourcing

From a hiring and resourcing perspective, these top skills illustrate how the role of the Business Analyst in IT has evolved beyond simply documenting business needs. Today’s most in-demand BAs combine deep analytical capabilities, technical fluency, and strong interpersonal acumen. For recruiters, this means expanding candidate assessments to evaluate not only traditional business analysis experience but also data literacy, understanding of technical ecosystems, and proven ability to drive engagement across diverse stakeholder groups.

Organisations that align hiring strategies with these market-driven skills are more likely to build BA teams that contribute directly to strategic outcomes rather than just facilitating requirements. In a competitive talent landscape, prioritising these capabilities in job descriptions, interview frameworks and career development pathways helps ensure a strong pipeline of talent that can support sustainable digital value delivery.

In today’s competitive tech hiring market, attracting strong IT candidates is not just about salary. It is about clarity, credibility and alignment. Many organisations believe they have a talent shortage problem. In reality, they often have a messaging problem. Poorly written IT job descriptions quietly push away the very candidates hiring managers want most.

Recent hiring research shows that job seekers are increasingly selective, especially in software, cloud and cybersecurity roles. Candidates compare multiple offers, analyse company culture signals and look for transparency before applying. According to the Future of Recruiting report by LinkedIn, candidates are placing greater value on skills clarity, growth opportunities and flexible working arrangements when deciding where to apply. When job descriptions fail to communicate those elements clearly, applications drop.

Below are the most common reasons IT job descriptions fail to convert high-quality candidates into applicants.

1. The Role Is Too Vague

Top IT professionals want to know exactly what problem they are being hired to solve. Generic phrases such as “responsible for managing systems” or “working in a fast-paced environment” do not communicate real impact. Skilled engineers and analysts look for technical depth and measurable outcomes.

Hiring data shows that clear skills-based descriptions significantly improve application quality. When requirements are ambiguous, experienced professionals often move on to clearer opportunities.

2. The Requirements List Is Unrealistic

Many IT job ads still ask for ten years of experience in technologies that have only existed for five. Overloaded requirement sections discourage even qualified candidates from applying.

“Stop asking for 10+ years of experience or a specific college degree like it’s the only way to succeed.” – Forbes

High-performing engineers often skip roles that appear rigid or unrealistic, especially in markets where demand exceeds supply.

3. There Is No Mention of Career Growth

Digital professionals want to build long-term skills, not just complete tasks. If a job description focuses only on daily responsibilities without addressing growth pathways, training support or exposure to new technologies, it signals stagnation.

This survey  from Deloitte found that career development opportunities are one of the top drivers influencing tech job decisions. A job description that fails to mention growth sends a clear message, even if unintentionally.

4. Compensation Transparency Is Missing

Salary transparency is no longer optional in many markets. Even where it is not legally required, candidates expect at least a salary range.

A hiring analysis from Indeed showed that job postings including salary ranges received significantly higher engagement than those without. In competitive IT sectors, omitting pay details can reduce applicant numbers dramatically.

5. The Language Is Too Corporate

Overly formal, buzzword-heavy descriptions feel outdated. Tech professionals respond better to straightforward, human language that reflects how real teams operate.

Candidates often scan job posts quickly. If the tone feels impersonal or filled with vague corporate jargon, it can reduce trust and authenticity. Clear, direct language performs better for both SEO and conversion.

6. Remote and Hybrid Flexibility Is Unclear

Flexible working has become standard expectation rather than a perk. If your job description does not clearly explain remote, hybrid or office expectations, candidates may assume the worst.

Data from McKinsey & Company used by HRDrive in this article showed that flexibility remains a critical factor in tech talent retention and attraction. Ambiguity around location policies creates friction at the very first stage of engagement.

7. The Tech Stack Is Not Clearly Defined

Experienced IT professionals care deeply about tools and architecture. They want to know whether they will work with modern cloud platforms, legacy systems or emerging AI frameworks.

Clear stack visibility signals technical maturity. It also improves search visibility, since candidates often search for roles by specific technologies rather than generic titles.

8. There Is No Sense of Impact

High-quality candidates want to know why the work matters. Does the role contribute to product innovation, digital transformation, cybersecurity resilience or customer experience improvements?

The Job Seeker Trends report from CompTIA highlighted that purpose-driven messaging increasingly influences tech career decisions. If impact is missing from the description, motivation drops.

9. The Hiring Process Feels Slow or Opaque

If a job description does not outline next steps or timelines, candidates may assume a slow process. In fast-moving IT markets, strong candidates are often off the market within weeks.

Transparency about interview stages and decision timelines signals organisational efficiency. Without it, candidates may prioritise competitors with clearer communication.

10. The Description Is Written for Compliance, Not Conversion

Many IT job descriptions are written to satisfy internal HR templates rather than attract people. They focus heavily on internal reporting lines, policy statements and compliance language while neglecting candidate experience.

Modern hiring is part marketing, part assessment. A job description is not just a legal document. It is a conversion tool. When written with clarity, authenticity and strategic positioning, it becomes one of the most powerful levers in attracting high-calibre tech professionals.

Final Thoughts: Job Descriptions Are Strategic Hiring Assets

In 2026, the IT talent market is competitive, selective and skill-driven. Organisations that treat job descriptions as strategic marketing assets outperform those that rely on outdated templates. Clear skills alignment, growth visibility, compensation transparency and authentic tone are no longer optional. They are baseline expectations.

If your IT job descriptions are not attracting strong candidates, the issue may not be a talent shortage. It may be how the opportunity is being presented.

As organisations move away from delayed reporting and towards live decision-making, real-time and streaming data engineers are becoming some of the hardest roles to hire. These engineers sit at the centre of systems that power fraud detection, live dashboards, operational monitoring and personalised customer experiences. From a resourcing perspective, the challenge isn’t just finding data engineers, it’s finding people with the right blend of real-time, cloud and operational skills.

Below are the five skills hiring teams should prioritise when recruiting streaming data engineers in 2026, explained in practical, non-technical terms.

1. Hands-On Experience With Streaming Frameworks

The most obvious and consistently requested skill is real experience with streaming platforms such as Apache Kafka, Apache Flink or Spark Streaming. These tools allow data to be processed continuously instead of in daily or hourly batches, which is what makes real-time intelligence possible. From a hiring perspective, candidates who have actually built and supported streaming pipelines in production are far more valuable than those with only theoretical exposure. Employers increasingly specify these tools in job descriptions because they underpin almost every modern streaming architecture.

“A focus on delivering hyper-personalized experiences, reducing operational latency, and deploying AI-powered insights across supply chains underscores the critical role of advanced streaming analytics platforms in maintaining competitive advantage.” – GlobeNewswire

2. Strong Programming and SQL Foundations

Despite the rise of specialised platforms, streaming data engineers still rely heavily on solid programming fundamentals. Languages such as Python, Java and Scala are commonly used to build, customise and maintain real-time pipelines. Alongside this, strong SQL skills remain essential for shaping data, validating outputs and making streaming results usable for analytics and reporting teams. For resourcing teams, this means avoiding candidates who specialise only in tooling and instead prioritising those with strong core engineering skills that will remain relevant as technologies evolve.

“The company says that when software application developers and data engineering professionals lack contextualized and trustworthy data, driving meaningful AI innovation becomes impossible.” – Forbes

3. Understanding of Event-Driven and Distributed Systems

Real-time systems behave very differently from traditional applications. Data arrives out of order, systems fail mid-stream, and performance issues can cascade quickly. This is why organisations are placing more value on engineers who understand event-driven architectures and distributed system behaviour. From a hiring standpoint, this skill helps teams avoid costly outages and performance bottlenecks by ensuring engineers know how to design systems that can recover gracefully and scale reliably.

4. Cloud and Platform Integration Skills

Most real-time data systems now run in cloud or hybrid environments, which makes cloud fluency a hiring priority. Streaming data engineers are expected to integrate pipelines with services such as AWS Kinesis, Azure Event Hubs or Google Cloud Pub/Sub, while also connecting to data lakes, warehouses and analytics platforms. For recruiters, this means looking beyond on-premise experience and prioritising candidates who understand how streaming fits into modern cloud ecosystems and managed services.

“This integration gap prevents organizations from leveraging AI effectively, as models require access to comprehensive, real-time data.” – Integrate.io

5. Operational Awareness and Monitoring Capability

Because streaming systems run continuously, operational awareness is no longer optional. Engineers must know how to monitor pipelines, detect issues early and respond before business users are affected. Skills in observability tools, alerting, logging and automation are increasingly mentioned in job ads for streaming roles. From a resourcing perspective, this is where many teams underestimate effort — hiring engineers who can operate and support live systems reduces long-term risk and prevents burnout across teams.

Why These Skills Matter for Hiring in 2026

For hiring managers and workforce planners, streaming data engineering is no longer a niche specialism. It’s becoming a core capability that supports real-time business operations across multiple departments. Teams that recruit for these five skills early are better positioned to deliver reliable live insights, set realistic project timelines and avoid overloading traditional data or cloud teams with responsibilities they weren’t designed for.

As demand continues to grow, organisations that understand what to look for, and why will have a significant advantage in attracting and retaining the right talent.

Delivering a new target operating model in a midsize business is a significant undertaking. It isn’t just about rewriting process diagrams or deploying new technology. It’s about reshaping how the organisation functions; how people work, make decisions, and create value. For hiring and resourcing teams, understanding what it takes to successfully deliver a new operating model is crucial. The way a business designs, staffs, and supports the transition often determines whether the changes stick or fade away after the initial launch.

What Is a Target Operating Model and Why It Matters

A target operating model describes how a business organises itself to deliver its strategy. It typically includes business processes, organisational structure, technology, governance, and metrics. When a business decides to change its operating model, it’s usually because existing ways of working no longer support growth ambitions, competitive pressures, or transformation goals.

Industry analysts describe operating model change as a key part of business transformation, especially for organisations looking to scale or innovate in fast-moving markets.

People Are at the Heart of Change

One of the first things hiring teams should recognise is that delivering an operating model change is fundamentally a people-centric challenge. While technology platforms and process redesigns are visible parts of the change, real success depends on how people adopt new ways of working. Talent resourcing strategies need to anticipate not just the skills required to build the model but also the capabilities to sustain it, from leaders who drive cultural alignment to operational teams executing daily work under the new model.

“In many cases, they are looking beyond previously established people management practices and people function (or HR) operating models and taking a step into the future of people management.” – McKinsey

Balancing Flexibility and Expertise in Midsize Businesses

For midsize businesses, one of the biggest considerations is balance. These organisations often sit between the agility of smaller companies and the complexity of larger enterprises. They may have fewer specialised roles but greater flexibility to shift priorities quickly.

“However, the key to a successful transformation lies in more than just technology implementation. It requires a well-defined Target Operating Model (TOM) that acts as a strategic guide, ensuring operations are optimized to drive growth and address future challenges effectively.” – SAPinsider

This means hiring and internal resourcing must be purposeful and aligned with what the new operating model aims to achieve. If a business is moving toward a customer-centric model, resourcing teams may need to strengthen roles in customer success, data analytics, and digital engagement, not just traditional operations or IT.

Governance, Metrics, and Measuring Success

Delivering a new operating model requires clear governance and value measures. Organisations should define success early and agree on metrics that matter, spanning customer outcomes, efficiency gains, financial performance, or employee engagement. Hiring teams should identify talent who can execute and also track, report, and optimise value delivery. Success metrics should be reinforced through coaching, incentives, and leadership behaviours.

External Expertise and Hybrid Roles

A common challenge in midsize businesses is that internal teams may lack experience with large-scale transformation initiatives. External expertise and hybrid roles often become critical. Organisations frequently bring in transformation leads, operating model specialists, or consultants to help design and implement changes while mentoring internal staff. These hybrid engagements help build internal capability while ensuring execution gaps don’t stall the project.

Technology, Tools, and People Integration

Midsize businesses may adopt new platforms, automation, or data tools as part of the operating model shift. Technology alone does not drive change; success comes when teams understand how to use these tools effectively. Resourcing practices should ensure that roles with both technical proficiency and operational context are present. This includes upskilling around data literacy, cloud solutions, workflow automation, or digital collaboration tools, depending on the model’s direction.

“Perhaps the biggest change is the realisation that the operating model is no longer static. Leading organisations now treat it as a continuously evolving system that is enhanced based on strategic priorities, external shifts, and internal learning.” – k3

Communication and Change Leadership

Communication plays a central role in operating model delivery. Employees need clarity on what is changing, why it matters, and how their daily work will be different. Hiring and resourcing teams can support this by ensuring change leadership, internal communications, and organisational development skills are present in the project team. Roles focusing on employee experience, stakeholder engagement, and organisational readiness help adoption happen faster and with less resistance.

Timing, Phasing, and Lifecycle Resourcing

Delivering a new operating model unfolds in phases: design, build, test, transition, and full adoption. Each phase benefits from different skills and capabilities. Staffing often focuses on design and planning early, but execution and sustainment capabilities may be overlooked. Hiring strategies that account for the entire lifecycle of the operating model, from design to run, will be more robust and less reactive.

Leadership Alignment and Accountability

Successful operating model change depends on leadership alignment. Leaders must not only support the transformation but embody it. Hiring for leadership roles should prioritise individuals with experience shaping culture, leading change, and holding teams accountable. Leadership reinforces new behaviours and ensures the operating model becomes embedded in day-to-day operations.

Conclusion: Aligning Talent with Transformation

Delivering a new target operating model in a midsize business is complex but achievable when organisations consider people, process, technology, and leadership holistically. For hiring and resourcing teams, it’s not enough to fill roles; teams must be intentionally shaped to support both the transition and the long-term model. Organisations that anticipate skill needs, invest in capability building, and align talent with strategic outcomes are better positioned to realise the value that operating model change promises.

As we move into 2026, organisations across industries are integrating artificial intelligence deeper into products, services, and ways of working. This shift is reshaping hiring priorities, meaning recruiters and resourcing teams are now looking for a blend of technical know-how, strategic thinking, and human-led oversight when filling AI-related roles. Understanding the key skills employers want can help hiring managers target the right talent and help professionals prepare for the most relevant opportunities.

1. Machine Learning Fundamentals and Model Understanding

Machine learning remains the core of most modern AI systems. Employers are looking for people who understand how models learn from data, how to evaluate their accuracy, and how to tune them for better performance. This skill isn’t just for specialists; even non-technical team members benefit from knowing the basics of supervised and unsupervised learning if they work with data-driven systems. Sources indicate that recognising how models work helps teams interpret outputs more wisely and collaborate better with technical leads.

2. Prompt Engineering and AI Workflow Orchestration

As generative AI tools become embedded in workflows, knowing how to shape effective prompts and integrate AI into broader processes is becoming essential. Prompt engineering has evolved from simply asking questions to structuring multi-step workflows that automate tasks and connect tools, data, and decisions. Professionals who can turn AI from a standalone tool into a scalable, integrated part of business processes are increasingly in demand.

3. Data Literacy and Feature Engineering

AI systems are only as good as the data fed into them, so data literacy (the ability to clean, interpret, and structure data) is one of the most sought-after skills. Hiring teams want candidates who can work with imperfect datasets, reduce bias, identify meaningful features, and ensure that AI outputs are grounded in high-quality inputs. This skill cuts across many roles because data is the backbone of AI deployment.

4. AI Governance and Responsible AI Practices

With AI becoming embedded in products and processes, organisations are increasingly focused on ethical, transparent, and compliant use of these technologies. Skills that cover bias mitigation, explainability, model monitoring, and compliance frameworks are now part of core hiring criteria for AI teams. Understanding where AI fails or misbehaves is just as important as knowing how to build it.

“The AI talent market has experienced unprecedented growth in 2025, with job postings increasing 74% year-over-year according to LinkedIn’s Global Talent Trends report. This surge comes despite broader tech industry layoffs, highlighting AI as a recession-resistant sector driving continued hiring.” – Hakia

5. Cloud and MLOps Infrastructure Skills

Deploying AI models at scale means understanding how to operate them reliably in production environments. This includes expertise with cloud platforms (such as AWS, Azure or GCP) and AI infrastructure tools that support continuous integration and deployment, monitoring, and version control of models. MLOps (machine learning operations) bridges engineering and operations to make AI systems reliable and robust for business use.

“Job postings mentioning Google Cloud rose from about 3 % to over 5 % in a year, while AWS mentions increased from over 12 % to nearly 14 %. Companies are migrating workloads and need engineers comfortable with containers, microservices and serverless functions.” – Cogent University

6. Programming Languages and Framework Fluency

Technical fluency remains a foundation. Languages like Python dominate AI roles, given their versatility and extensive libraries for machine learning and data analysis. Frameworks such as PyTorch and TensorFlow are widely used, and familiarity with them helps engineers build and refine AI systems efficiently. Recruiters often screen for this fluency because it signals readiness to contribute from day one.

7. Natural Language Processing (NLP) and Multimodal Skills

Ability in NLP (making machines understand and generate human language) continues to be a high-value skill as chatbots, virtual assistants, and conversational AI grow more common. Beyond text, multimodal skills that enable AI to work across text, images, audio, and more are becoming increasingly relevant for interactive and immersive user experiences.

8. Recommendation Systems and Personalisation Expertise

AI isn’t only about understanding data; it’s also about tailoring experiences. Recommendation systems help personalise content, products, and interactions for users, driving engagement and growth in sectors like e-commerce, media and SaaS platforms. Professionals who know how to design and tune these systems can make measurable business impact.

“Jobs requiring experience working with ‘recommendation systems’ offer the highest median salaries.” – Yiba

9. Distributed Systems and Performance Optimisation

Modern AI applications often run on distributed systems that must handle heavy loads and deliver responses in real time. Understanding how to design efficient distributed architectures and optimise performance helps organisations scale AI work without crippling latency or cost overruns. This skill is especially relevant to high-performance computing and large user bases.

10. Strategic Thinking and Change Management

Technical skills are crucial, but organisations increasingly recognise the importance of strategic and people skills in making AI initiatives succeed. Professionals who can guide cross-functional collaboration, manage organizational change and align AI projects with business outcomes are increasingly valuable. These higher-order skills help ensure AI delivers real value rather than becoming siloed or underutilized.

What This Means for Hiring and Resourcing in 2026

Hiring teams in 2026 are looking for more than just technical expertise. They want individuals who can bridge business needs and AI capabilities, support ethical and responsible use, and integrate AI into real-world workflows. As AI continues to transform roles and industries, building talent strategies around these ten skills can help organisations attract, retain, and grow the right people.

As we move through 2026, the technology job market is shifting in ways that matter for hiring, resourcing, and planning ahead. Some roles that looked promising a few years ago have now become core to business strategy, while organisations are investing more in skills that help them stay resilient, competitive and ready for change.

Based on current hiring trends and labour market demand, five tech roles stand out as the most sought after this year, and they reveal a lot about where teams are focusing their energy.

1.    Cybersecurity Engineer / Analyst: Security First, Always

One of the most persistent trends heading into 2026 is the demand for cybersecurity talent. As organisations embrace digital transformation and expand their cloud, mobile, and data-driven platforms, the importance of securing systems has never been greater. Job postings and industry insight consistently show that cybersecurity analysts and engineers are topping demand lists, with organisations prioritising skills in threat detection, secure architecture, and incident response.

Growing breaches, regulatory pressure, and the need for proactive protection mean that teams need specialists who can help shape safer delivery pipelines and reduce risk across the organisation. This demand reflects a broader pattern where security expertise is becoming foundational rather than optional.

“As AI becomes embedded across industries, it’s driving expansion in cybersecurity, data sharing and a new generation of hybrid roles that combine technical skill, strategic judgement and ethical awareness.” – LSE

2.    Cloud Engineer / Cloud Architect: Building the Backbone of Digital Services

Cloud computing remains central to modern IT strategy, and with nearly all new workloads projected to run on cloud-native platforms, engineers who can design, manage and optimise cloud infrastructure continue to be in hot demand. Cloud engineers and architects are expected to be well-versed in AWS, Azure, Google Cloud Platform, hybrid configuration, and performance/cost optimisation.

For hiring teams, this means planning ahead for professionals who can help organisations scale securely and efficiently, especially as workloads move further away from on-premise models and more into distributed, multi-cloud ecosystems. Organisations are looking for candidates who can translate business needs into cloud strategy, manage migrations, and build resilient systems that support ongoing growth.

“Cloud skills appear in 85% of tech job postings, making them virtually mandatory for modern developers.” – Hakia

3.    DevOps Engineer / Platform Specialist: Enabling Fast and Reliable Delivery

Another role gaining momentum in early 2026 is the DevOps engineer, often paired with platform engineering responsibilities. While DevOps has been a well-established discipline for some time, the focus has shifted toward combining delivery speed with stability and quality. Teams are hiring DevOps and platform engineers who can build and maintain CI/CD pipelines, automate infrastructure, and support continuous delivery practices.

These skills are crucial for organisations that want to remove bottlenecks between development and operations, foster collaboration across teams, and ensure that new features and updates reach users quickly without sacrificing reliability. DevOps expertise also aligns closely with hybrid cloud and microservices environments, making it a versatile and strategic addition to modern IT teams.

4.    Data Engineer / Analytics Specialist: Turning Data Into Competitive Advantage

Data remains a driving force in how businesses make decisions, spot patterns, and optimise performance. Specialised data engineers and analytics professionals are in high demand because they enable organisations to collect, clean, transform, and action complex datasets. Data engineering roles often support real-time analytics, data lakes, warehouse strategy, and the infrastructure needed to power machine learning or business intelligence.

Hiring and resourcing teams are prioritising these skills because companies of all sizes now rely on data proficiency to inform strategy, from operations and financial planning to customer experience and product development. These roles often sit at the intersection of technology, business insight, and strategic decision-making.

“The WEF ranks analytical thinking as the top core skill, while the CIO study shows analysis skills were required in over 19 % of tech postings in 2024 and over 21 % in 2025. Data science careers are expected to grow 34 % over the next decade.” – Cogent University

5.    UX / Human-Centred Design Roles: Making Technology Work for People

Finally, UX (user experience) and human-centred design roles have risen in importance as organisations emphasise adoption, usability, and customer satisfaction. Although UX roles are sometimes overshadowed by more technical specialisations, they are increasingly in demand because software success now depends on intuitive interfaces and user journeys that help customers realise value quickly. Hiring teams are looking for UX professionals who can collaborate with product management, engineering, and business stakeholders to ensure experiences are clear, accessible, and aligned with strategic goals.

As the tech landscape becomes more competitive and user expectations rise, investing in UX talent helps organisations retain customers and reduce friction in digital interactions.

What This Means for Hiring in 2026

The common thread across these five roles is that they are not just technical jobs, but roles that tie directly to business outcomes. Cybersecurity protects trust and continuity. Cloud architects build the flexible platforms modern companies rely on. DevOps and platform specialists help teams move faster with confidence. Data engineers turn information into insight. UX designers make technology usable and valuable for people.

From a resourcing perspective, this means planning beyond surface job titles and considering how these roles interact with strategy, delivery, risk, and customer satisfaction. Organisations that align hiring with these priorities will be better positioned to adapt to change, innovate safely, and build products that users actually want to use.

When most people hear “blockchain,” they think of Bitcoin and other cryptocurrencies. But in 2026, organisations are increasingly exploring blockchain for applications outside of digital money, especially where security, transparency, identity, and trust matter. This broader adoption matters not just to technology teams, but also to hiring and resourcing leaders who are planning future capabilities.

Blockchain is being used to support secure digital identities, build transparent supply chains, verify product origin (provenance), and even manage enterprise compliance in new ways. These emerging use cases are creating demand for specialised skills and new kinds of roles in IT and operations teams.

“This shift reflects a broader understanding that blockchain is not merely a financial tool, but a transformational business platform.” – WebcomSystems

Supply Chain Transparency and Provenance Bring New Skills into Play

One of the clearest examples of blockchain outside cryptocurrency is in supply chain management and product provenance. Enterprises are using distributed ledgers to create tamper-proof records of goods as they move from raw materials to the finished product delivered to customers. This not only improves traceability and accountability, but also enhances compliance with regulations and helps brands prove ethical sourcing.

For hiring leaders, this trend translates into opportunities and challenges. Organisations need people who understand how to integrate blockchain with existing enterprise systems, how to ingest data from sensors or partners, and how to interpret and use immutable records for real-time decision making. Skills in blockchain architecture, distributed systems engineering, and integration with IoT or ERP platforms are increasingly valuable.

Digital Identity and Decentralised Verification

Another blockchain use case beyond crypto is secure digital identity, often called self-sovereign identity (SSI). Instead of storing credentials in centralised databases (which are high-risk targets for breaches), blockchain lets individuals and organisations hold and verify identity credentials cryptographically. This means better privacy, reduced fraud, and greater control for users.

For resourcing teams, identity use cases create demand for people who understand decentralised identifiers (DIDs), verifiable credentials (VCs), cryptography, and privacy-preserving systems. These are not typical skills on every IT resume, so organisations often find they need to invest in training, cross-skilling, or targeted recruitment to build teams capable of delivering secure identity solutions.

“By 2025, over 20 million citizens in countries like Estonia and Singapore use blockchain-based IDs for government services, per a 2025 World Bank report.” – Webiii3

Provenance and Anti-Counterfeiting

Blockchain’s ability to record an asset’s entire history (who touched it, when, and how it changed) is known as provenance. This is valuable in industries where authenticity is crucial: pharmaceuticals, luxury goods, electronics and industrial materials all use blockchain to track provenance and thwart counterfeit products.

This trend matters for recruiting because organisations need professionals who can build interfaces between business users and distributed ledgers, design data models that are both efficient and secure, and ensure that blockchain systems work reliably with external partners.

Tokenisation and New Business Models

Beyond identity and supply chain, blockchain enables tokenisation, the representation of real-world assets as digital tokens on a ledger. This can make assets like real estate, commodities or intellectual property easier to trade, divide or manage. This capability is seen spreading into corporate finance and operations, creating demand for roles that blend blockchain engineering with financial systems knowledge.

From a staffing perspective, bridging traditional business functions with technical blockchain expertise requires hybrid profiles, people who understand both the business logic of assets and how to represent them securely on a distributed ledger.

“Tokenization, aka converting assets into digital tokens, results in fractional ownership, quicker fundraising, and global investor participation.” – Hyperlink InfoSystem

What This Means for Hiring and Resourcing

The broader uptake of blockchain beyond cryptocurrency has several important implications for hiring and resourcing teams. Organisations are increasingly finding that blockchain initiatives require new competency areas that go beyond traditional software development. Roles focused on blockchain strategy, architecture, systems integration, and governance are becoming more relevant, particularly where decentralised identity, data provenance, and enterprise assurance are involved.

At the same time, the most in-demand profiles tend to be cross-functional. Professionals who understand security, cloud platforms, and distributed systems together are often more valuable than specialists who operate in only one domain. Many organisations discover that existing software engineers need additional training in cryptographic concepts, ledger design, and decentralised trust models to effectively contribute to blockchain projects.

Because enterprise blockchain use cases are still evolving, many teams are choosing to invest in internal upskilling rather than expanding headcount immediately. Structured training, pilot programmes, and partnerships with specialist vendors are common approaches to building capability while reducing delivery risk. Organisations that fail to plan for these skill requirements often end up reacting late, struggling to staff projects once timelines are already under pressure. This can delay adoption and undermine the very trust, transparency, and compliance benefits that blockchain is intended to deliver.

Conclusion

Blockchain’s reach has grown far beyond cryptocurrencies into practical, enterprise-focused use cases like secure identity, transparent supply chains, digital provenance, and tokenisation. These shifts are reshaping how IT and business teams operate and, equally importantly, how they hire.

For recruiters, IT leaders, and resourcing teams in 2026, understanding these trends and the associated skills is essential. The ability to plan and attract talent with blockchain architecture, decentralized identity, systems integration, and secure distributed systems expertise will make the difference between teams that deliver value and those that struggle with adoption.