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Azure Data Engineer

An Azure Data Engineer builds and maintains the systems that move, clean and store an organisation's data using Microsoft Azure — Microsoft's cloud computing platform, where storage and processing happen on rented servers rather than machines the company owns. The job is mostly writing code and configuring cloud services so that data from sources like sales systems, apps, sensors or finance software arrives somewhere reliable, in a usable shape, for analysts, data scientists and reporting tools to use. Most graduates doing this work sit either in the in-house data team of a bank, retailer, insurer, NHS body or utility, or in a consultancy that builds these systems for client organisations.

Starting salary
£28k–£40k
Live graduate jobs
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Main way in
Graduate schemes in technology, data or engineering at large…
They usually ask for
A degree is usually expected

Approximate graduate salary

Broadly, graduate and junior data engineering roles in the UK tend to start somewhere around £28,000–£40,000, though this varies a great deal. London and large financial-services or consultancy employers sit at the higher end; smaller firms, public sector bodies and roles outside the major cities sit lower. Pay in this area tends to rise fairly quickly with a few years of demonstrable experience, and contract day rates for experienced engineers are a separate market again.

What you'd actually do

  • Writing and testing data pipelines — automated jobs that pull data from a source system, reshape it, and load it into a central store. In Azure this usually means building these in Azure Data Factory or Synapse, or writing them in Python/PySpark notebooks in Databricks.
  • Writing SQL. A lot of SQL. Joining tables, aggregating figures, checking that yesterday's load produced the row counts you expected, and debugging why a transformation has quietly dropped records.
  • Investigating failed overnight jobs: reading error logs, working out whether a source system changed its file format, the data was late, or someone's code broke, and then fixing or re-running it.
  • Taking a request from an analyst or a business team — 'we need daily figures on returns by store' — and working out which source systems hold that data, how reliable it is, and how to model it into tables people can query.
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  • Putting code into version control (Git) and through deployment pipelines so changes move from a development environment to production safely, rather than being edited live.
  • Attending stand-ups and sprint planning, as most data teams run some form of agile working, and writing up documentation so other people can understand what a pipeline does.

How graduates get in

  • Graduate schemes in technology, data or engineering at large employers — banks, insurers, retailers, telecoms, energy companies, the NHS and government departments. These often rotate you across data engineering, analytics and software roles before you specialise, so you may not be labelled 'Azure Data Engineer' on day one.
  • Technology and data consultancies, which hire graduates in volume and are a very common route in. You get exposed to several clients and cloud platforms quickly, which builds experience fast, though you may be placed on whatever the client's stack happens to be.
  • Direct entry into a junior or associate data engineer post advertised by a single employer. Less structured than a scheme but increasingly common, especially outside London.
  • Moving sideways from a first job as a data analyst, BI (business intelligence) developer, or software developer. This is probably the single most common way people end up with 'Azure Data Engineer' in their title, since the skills overlap heavily and employers like hiring internally for it.
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  • Degree apprenticeships and higher apprenticeships in data engineering or software, which are an alternative to the graduate route rather than a route for people who already have a degree — though some employers do take graduates onto these.
  • Self-directed entry: building projects, completing Microsoft's Azure certifications, and applying to smaller firms. This works but is slower and much more dependent on having something demonstrable to show.

What employers ask for

  • A degree is usually expected, but the subject matters less than you'd think. Computer science, software engineering, maths, physics, engineering and economics are all common; so are conversion routes. What employers actually screen for is evidence you can code and reason about data.
  • SQL competence is close to non-negotiable, even at graduate level. Many employers test it at interview with a live exercise.
  • Python is the usual second requirement. Some employers accept Scala or Java instead, and a minority of Microsoft-heavy shops still lean on C# or heavy use of SQL Server tooling.
  • Grade requirements vary a lot. Large graduate schemes often ask for a 2:1 and sometimes UCAS points; many direct-entry and smaller employers care only that you can pass the technical exercise. Don't assume a 2:2 rules you out of the whole market.
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  • Microsoft Azure certifications (notably the Azure Data Fundamentals and Azure Data Engineer Associate exams) are useful signals for direct entry and for consultancies, which often have targets for certified staff. They are rarely required for graduate schemes, which will train you.
  • Prior exposure through a placement year, summer internship or a personal project that actually moves and transforms real data. A project that pulls from a public API, stores the data and builds something on top of it carries more weight than another tutorial-following exercise.

Skills that matter

SQL, to a genuinely good standard

Almost every pipeline ends in a database, and most of your debugging involves querying data to find out where the numbers went wrong.

Python (and ideally PySpark)

Transformations that are awkward in SQL get written in Python, and Spark is the standard tool for processing data too big for one machine.

Data modelling

Deciding how to structure tables — what the grain of a table is, how facts relate to dimensions — determines whether analysts can answer questions easily or have to keep coming back to you.

Systematic debugging

Pipelines fail for dull reasons — a changed column name, a duplicate key, a time zone — and the job rewards people who work through logs methodically rather than guessing.

Software engineering discipline (Git, testing, code review, CI/CD)

Data engineering has moved towards being treated as proper software development, and employers increasingly expect pipelines to be version-controlled and deployed automatically rather than hand-edited.

Translating a vague business request into a data specification

Stakeholders ask for 'customer numbers' without defining what counts as a customer, and getting that pinned down before you build is the difference between a useful pipeline and a rewrite.

Cost awareness in the cloud

Azure bills by what you consume, so an inefficiently written query or an over-provisioned cluster has a direct and visible financial cost that someone will ask you about.

Putting these on a CV? We counted which skills 12,914 live graduate adverts ask for, and what to do with the answer — part of our application advice.

Where it leads

  1. Graduate or junior data engineer, typically the first year or two: working on well-defined pieces of existing pipelines, with your code reviewed before it goes live. On a structured graduate scheme this may include rotations and funded certifications.

  2. Data engineer: owning pipelines end to end, being the person who gets called when they break, and starting to make design decisions about how data is modelled and stored. Timeframes vary widely — in a fast-growing team or consultancy this can come quickly; elsewhere it takes longer.

  3. Senior data engineer: designing the architecture rather than implementing someone else's, setting standards for the team, reviewing others' code and mentoring juniors.

  4. From senior, the path forks. Common destinations are lead or principal engineer (staying technical), data architect (designing the whole data estate across an organisation), engineering manager (people and delivery), or a move sideways into analytics engineering, machine learning engineering or platform/DevOps work.

  5. Consultancy adds a parallel ladder — consultant, senior consultant, manager — where progression depends as much on client relationships and selling work as on technical depth. Contracting is also common for experienced Azure engineers, with higher day rates but no job security.

What people get wrong

“It's a data science job — you'll be building machine learning models and finding insights.”

It's much closer to software engineering and plumbing. You build the systems that make data usable so that other people can analyse it. If you want to build models and interpret results, data science or analytics is the role you want, not this one.

“Because it says 'Azure', you need to know Azure before you apply.”

Employers hiring graduates generally care about SQL, coding ability and how you think about problems. The Azure-specific tooling is learnable in weeks and most will train you. The 'Azure' in the title usually just reflects which cloud that employer happens to have bought into — and plenty of engineers move between Azure, AWS and Google Cloud during their career.

“It's a solitary, heads-down coding role with minimal contact with other people.”

A large share of the job is working out what people actually need, chasing down who owns a source system, explaining why a number differs from the one in last month's report, and negotiating what's realistic. Engineers who can't do this part stall quite early.

“Cloud means you no longer have to worry about infrastructure.”

You worry about different things — access permissions, networking between services, how much compute a job is consuming, and the monthly bill. Cost and performance tuning become part of the engineering job rather than someone else's problem.

Where this varies

The job varies substantially depending on where you do it. In a consultancy you move between clients every few months, work across whatever tooling each one has, and spend more time on documentation, handover and client meetings. In an in-house team you live with the consequences of your own design decisions for years, which teaches you different lessons. Sector matters too: in banking and insurance, a lot of the work is governance, lineage (tracking where every figure came from) and regulatory reporting, and change moves slowly because of controls; in retail and tech-led firms there is usually more freedom and faster iteration; in the NHS and government, data sensitivity, information governance and legacy systems shape much of the work. Team size also changes the role — in a small organisation one person may cover engineering, analytics and reporting, while in a large one you may work on a narrow slice of a big platform. Finally, 'Azure Data Engineer' is not a fixed specification: some posts are heavily Databricks and Spark, others are mostly Data Factory and SQL-based warehousing, and others are closer to Power BI and reporting work. Read the advert's tooling list carefully, as it tells you more than the title does.

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General guidance about the role across the UK market, not about any specific employer. Entry routes and requirements vary — always check the individual job advert.

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