Hiring guide
How to Hire a Data Engineer
Start by defining what you actually need
“Data engineer” covers a lot of ground. Before writing the role, decide whether you need a platform builder (pipelines, warehouse, reliability), a modelling specialist (analytics engineering, dbt), or someone senior enough to lead the function. Hiring the wrong shape is the most common and expensive mistake here.
What to screen for
- Production pipeline experience at real scale, not just tutorials.
- Strong SQL and a modern stack: dbt, Spark or similar, Airflow or Dagster, a cloud warehouse (Snowflake, BigQuery, Redshift).
- A pragmatic view on quality, governance and cost. Good data engineers own reliability, not just the build.
- Judgement. The strongest people know when a simple, reliable pipeline beats a clever one that no one else can maintain.
A genuine technical screen matters more here than almost anywhere, because a plausible CV tells you very little about whether someone can keep a platform running. We screen every candidate technically before you meet them.
Write the role to attract, not just filter
Data engineers weigh the problem and the platform as much as the salary. Be specific about what they’ll own, the scale of the data, the stack, and the impact on the business. A generic “we need a data engineer” post gets generic applicants.
Move quickly
Strong data engineers are scarce and rarely on the market long. Keep your loop short, give feedback fast, and make sure every candidate hears from a real person.
What it costs
See the UK tech salary report for current ranges, and our AI, ML & data recruitment page for how we hire in this space. Data engineering sits alongside most of the industries we hire for, from fintech to SaaS, so the shape of the role often depends on the sector as much as the stack. If you’re building a data team, talk to us and we’ll bring a technically screened shortlist.
FAQ
Frequently asked questions
What should I look for when hiring a data engineer?
Production experience building and operating data pipelines at scale, strong SQL and a modern stack (dbt, Spark, Airflow, a cloud warehouse), and a pragmatic view on data quality, governance and cost. Judgement matters as much as tooling: the best know when a simple, reliable pipeline beats a clever one.
What is the difference between a data engineer, an analytics engineer and a data scientist?
Broadly: data engineers build and run the pipelines and platform; analytics engineers model that data for the business (often in dbt); data scientists and ML engineers build models on top. Titles blur, so define what you actually need before you write the role.
How much does a data engineer earn in the UK?
In London in 2026, senior data engineers typically earn £85k–£120k base, with staff-level roles higher. Ranges run lower outside London and vary by industry, with fintech and AI-heavy companies paying a premium.