Hiring Data Engineers

Hiring a Data Engineer sounds straightforward until you start the process.

On paper, the brief looks simple. You need someone who can build pipelines, improve data quality, work in the cloud and help the business make better use of its data. In practice, though, a lot of hiring processes stall because the role has been scoped too broadly, the interview process tests the wrong things, or different stakeholders want completely different outcomes from the same hire.

That is one of the biggest reasons companies struggle to hire strong Data Engineers.

It is not always a talent shortage problem. Often, it is a definition problem.

The first issue: “Data Engineer” means different things to different businesses

A Data Engineer in one company might be focused on moving legacy reporting into a modern cloud platform. In another, they are building production-grade pipelines that feed machine learning models. Somewhere else, they are effectively acting as an Analytics Engineer, cleaning up transformation layers and improving trust in reporting.

All three businesses might advertise exactly the same title.

That is where hiring gets difficult.

Before going to market, it helps to answer one question clearly:

What do we actually need this person to do in the first 12 months?

If the answer is vague, the shortlist usually is too.

What strong Data Engineering briefs look like in 2026

The best briefs are outcome-led, not tool-led.

A lot of job descriptions still read like a shopping list:

• SQL
• Python
• Snowflake
• dbt
• Airflow
• AWS, Azure or GCP
• stakeholder skills
• CI/CD
• streaming
• governance experience

Individually, none of that is unreasonable. The problem is that it often describes three different people, not one.

A stronger brief usually starts with the business need.

For example:

• Are you modernising a legacy data estate?
• Are you trying to improve reliability and observability?
• Do you need cleaner data products for analytics teams?
• Are you building infrastructure to support AI or machine learning use cases?
• Do you need someone hands-on, or someone who can also influence architecture and roadmap?

When that is clear, the skill set becomes easier to define and the hiring process becomes faster.

The types of Data Engineer hires companies most commonly confuse

One of the biggest causes of wasted time in data hiring is combining very different profiles into one role.

1. Platform-focused Data Engineers

These are the people who help design and build the foundations. They are often strongest around infrastructure, orchestration, performance, scalability and cloud-native ways of working.

2. Product-aligned Data Engineers

These hires sit closer to stakeholders and delivery teams. They are often better suited to environments where data is being treated as a product and where business value needs to be delivered quickly.

3. Analytics Engineering-style profiles

Some businesses say they need a Data Engineer when, in reality, they need someone who can improve modelling, transformation and reporting trust. In those cases, the role may sit much closer to analytics engineering than platform engineering.

4. ML or AI-supporting Data Engineers

As more businesses invest in AI, there is increasing demand for engineers who can support data readiness, model pipelines, feature availability and production workflows. That does not make every Data Engineer an AI Engineer, but it does change what “good” can look like in some teams.

If these distinctions are not made early, businesses often attract the wrong market and lose time mid-process.

What good Data Engineers are being assessed on now

The strongest candidates are rarely just “good with tools”.

Technical depth still matters, of course. SQL remains fundamental. Python is still highly valuable in many environments. Cloud experience is often essential. But the better hiring processes go beyond checking whether someone has worked with a specific stack.

In 2026, the better questions are often:

• Can they explain the trade-offs behind the choices they made?
• Have they improved reliability, scalability or cost-efficiency?
• Can they work across engineering and business conversations?
• Do they understand how data is actually consumed downstream?
• Have they worked in a messy environment, not just a polished one?

That last point matters more than many hiring teams realise.

There is a big difference between someone who has inherited a mature platform and someone who has helped shape one. Both can be good hires, but they are not the same hire.

What companies still get wrong in interviews

It is easy to create an interview process that filters for people who interview well but does not properly test whether they can succeed in your environment.

Some of the most common issues we see are:

• interview stages that are too long
• different stakeholders assessing different versions of the role
• technical tests that are disconnected from the day job
• no clear distinction between must-haves and nice-to-haves
• waiting too long to align salary expectations with the market

Strong candidates, particularly in data engineering, do not usually stay available for long. If the process is unclear or slow, they will often disengage before the final stage.

Permanent or contract?

This depends on the problem you are trying to solve.

If you need someone to build long-term capability, improve standards and become part of the fabric of the team, a permanent hire is often the better option.

If you need specialist support for a migration, platform implementation or short-term delivery push, a contractor can be a very effective solution.

The mistake is using one by default without being honest about the real requirement.

Sometimes businesses say they want a permanent hire because that feels like the safer choice. But if the need is immediate and highly specialised, contract can be the better answer. Equally, some businesses lean on contractors for too long when what they really need is a long-term capability builder.

Salary is still important, but clarity is often the bigger issue

Yes, salary matters. Strong Data Engineers know where demand sits, and the better ones are usually not applying blindly to everything on the market.

But budget is not always the only blocker.

In many cases, businesses lose good candidates because:

• the role is too broad
• the value proposition is weak
• the brief changes mid-process
• the team cannot explain the impact of the hire
• the interview process creates doubt

Good candidates are not just assessing the pay. They are assessing whether the opportunity makes sense.

So, what does good look like now?

Good looks like clarity.

Good looks like knowing whether you need a platform builder, a product-minded engineer or someone closer to analytics engineering.

Good looks like a process that tests real capability, not just keyword familiarity.

Good looks like a brief that matches the actual business need rather than trying to cover every possible requirement in one hire.

And good looks like moving with intent when you find the right person.

Because in Data Engineering, the market is competitive, but it is often poor scoping, not just scarcity, that causes the biggest hiring delays.

Hiring Data Engineers in 2026?

If you are planning to hire and want a clearer view of what the market looks like, what good should look like for your team, or how to shape the brief before going live, KDR Talent Solutions can help.

We work with businesses across data and AI hiring, helping teams define roles properly and connect with the right talent in a competitive market.

Need help hiring a Data Engineer? Get in touch with the KDR team.