Data and AI Job Market Review

...but from our perspective as a recruitment business working across the UK, EU and USA, the reality is way more nuanced than the rhetoric around whether “the market is flying” or “the market has stalled” (we’ve heard both recently during conversations with our community)

What we’d say, candidly, is this that the market is active, but it is selective and that has consequences both on the hiring side as well as for those looking to move on in their career.

In the UK, the ONS view in January and February 2026 does not suggest any real rebound yet. In the January release, vacancies edged up to 734,000 for October to December 2025, which was only a modest increase on the previous rolling quarter. Then in the February release, vacancies moved to 726,000 for November 2025 to January 2026, slightly higher than the comparable earlier period, but lower than the 734,000 reported a month before. So, the direction of travel is not one of meaningful recovery, if anything, the ONS is telling us the market remains broadly flat, with small month-to-month movements rather than any genuine surge in hiring appetite.
 
That being said, while the ONS is telling us the UK vacancies market remains broadly flat, more nuanced data suggests the data and AI market is fragmenting rather than simply slowing, with Data Engineering, Governance and AI-related hiring proving more resilient than the wider job market. For example, in the UK, Data Engineer demand looks firmer than the broader vacancies backdrop: IT Jobs Watch shows 1,068 permanent UK vacancies in the six months to March 2026, up from 749 in the equivalent period a year earlier. Data Governance also looks relatively healthy at 1,165 vacancies, up from 797 a year earlier. That suggests demand has improved, but not to the point where employers can be loose on process or vague on what good looks like.

On the AI side, the trend is stronger again. Indeed’s data shows AI-related hiring continuing to grow even while wider hiring remains subdued. Its UK report said 5.6% of UK job postings mentioned AI as of the end of October 2025, the highest share on record at that point, while its US labour market update said the Indeed AI Tracker reached 4.2% of US postings in December 2025 and that nearly 45% of data and analytics postings now contain AI-related terms. So, AI is clearly outperforming the wider market in terms of hiring intensity and skill demand.
 
On a broader job market perspective, in the US, the picture is slightly better on activity. CompTIA reported that active employer postings for tech occupations rose to 505,045 in February 2026, including more than 230,000 new postings in the month, after a January increase from December. In other words, hiring intent improved as Q1 progressed, but it improved from a cautious base rather than from a position of strength. Building on that, data and ai roles were starting from a higher base and are outperforming the overall job market.

That backdrop explains what many of us in the market have felt on the ground, clients are hiring, but with far less room for error.

If we look at ITJobs Watch for example and take Data Management as a job title, it shows that UK vacancies citing Data Management dropped to 931 from 1,728 year on year, and median pay fell from £75,000 to £60,000. That is a meaningful reset. It doesn’t mean Data Management is unimportant. In fact we have seen quite the opposite within clients. But what I think is happening here is that employers are being more specific, instead of hiring generic “data management” capability, they are niching into sharper disciplines such as governance, quality, architecture, master data and platform-led engineering.

That is probably the biggest market pivot going into Q2:the era of broad, catch-all data hiring is fading. The era of targeted, outcome-led hiring is here.

On the AI side, the signal is stronger. LinkedIn’s latest labour market report also showed roughly 1.3 million AI jobs created globally between 2023 and 2025, including 177,000 AI Engineer roles, while forward-deployed engineer demand grew 42x and AI engineer demand 13x over that period.

That global picture is interesting because it tells us where budgets are going. The money is still flowing into AI, but increasingly into applied AI, AI infrastructure, and roles that connect engineering to commercial delivery. Reuters has reported major ongoing AI infrastructure investment from firms such as Oracle, Google, Nvidia and Databricks, even as some companies also trim headcount elsewhere or use AI to run leaner teams. So globally, the picture seems to be emerging that more AI equates to different jobs, different skill mixes, and more pressure on productivity.

That brings us to what we think happens in Q2 2026. We think we are heading into a quarter where the strongest employers keep hiring in a focused way, while others remain cautious and wait for more economic clarity. In the UK especially, the wider labour market is still fragile enough that I would expect measured hiring rather than aggressive expansion. In the US, I would expect continued demand for platform, engineering and AI-enabled roles, but with scrutiny on headcount and ROI staying high.

This creates an interesting hiring environment where it could be easy to confuse a fuller market with an easier market. What I would say to hiring managers is yes, candidate availability has improved in some areas. But the best Data Engineers, Governance specialists, MDM professionals and AI talent are still not hanging around for long, especially when the brief is clear and the business case is compelling. The companies winning right now are the ones doing three things well: defining the problem properly, running a tight process, and being realistic on value rather than trying to buy premium capability at discounted rates.

For candidates, we would say, this is not a bad market, but it is a less forgiving one. Generic profiles are struggling more. Clear specialism wins. In Data Engineering, that means demonstrating platform depth, production delivery and business impact, not just tool lists. In Data Management, it means being able to show where you sit across governance, quality, MDM, metadata, privacy or architecture, and what outcomes you have driven. In AI, it increasingly means being able to connect model capability to operational reality. Employers are far less interested in theory than they were 12 months ago.

Our overall read for Q2 is that the market has not switched off but it has evolved with Data Engineering staying relatively strong, Data Governance and MDM remaining important, particularly where regulation, trust and AI readiness intersect and AI continues to attract investment, but the winners will be people and businesses who can turn that investment into something practical. In a more mature market, clarity beats hype every single time.