How to become a data engineer in the UK as a graduate

Some graduates go straight into data engineering through a graduate scheme, a junior role or the level 5 data engineer apprenticeship, but few UK adverts are aimed at beginners, so starting as a data analyst and moving across is a practical alternative. Employers look for strong SQL and Python, data modelling, experience of building and testing pipelines, and some knowledge of a cloud platform. One or two working pipelines on GitHub, with tests and a clear README, show that you can do the work.

What does a data engineer do?#

Data engineers build and run the systems that move data from where it’s created to where it’s used. The government’s Digital and Data Profession Capability Framework says a data engineer “develops and constructs data products and services, and integrates them into systems and business processes”, building the data flows that connect operational systems with analytics and business intelligence tools. The level 5 data engineer apprenticeship standard describes the job as building systems that collect, manage and convert raw data into usable information for data scientists, data analysts and business intelligence analysts.

The skills it lists include:

• querying and manipulating data with tools such as SQL and Python
• adding automated checks that catch bad data before anyone uses it
• finding and fixing problems when a data pipeline fails
• telling the people who rely on the data when something is down, and what it affects

The difference from a data analyst is mainly where you spend your time. Analysts use data to answer questions and explain what they found. Engineers build the pipelines, databases and warehouses that get clean, reliable data to analysts and other systems. The framework also has an analytics engineer role in between, which develops and tests data models so that people can use the data, starting at trainee level. Not sure which suits you? Take the two-minute analyst or engineer quiz.

Can graduates become data engineers straight away?#

Some do, through a graduate scheme, an apprenticeship or one of the few junior roles. Our review of what the research says about data engineering jobs found that the role pays well and sits in a group of IT jobs rated in critical demand, but that very few adverts are aimed at beginners.

Even the first level is a skilled job. The government framework has four data engineer levels, and at the first a data engineer “delivers the designs set by more senior members of the data engineering community”. The apprenticeship standard expects someone who completes their own work to specification, with minimal supervision.

So apply for graduate and junior data engineering roles if you can show the skills, but don’t rely on them alone. A first job as a data analyst, business intelligence (BI) developer or software developer builds the same foundations, and you can move across as your engineering skills grow.

What skills do UK employers ask for?#

The Department for Education’s data engineer job specification is a good example of what UK employers ask for. It wants experience of building data pipelines on cloud platforms, programming in SQL and Python, delivering through DevOps practices such as continuous integration and deployment (CI/CD) and automation, and a good understanding of common problems in data and how to resolve them. It also expects you to work with product owners, architects, software engineers and data scientists to understand what data they need.

Adverts often name specific tools. ITJobsWatch, which tracks UK IT job adverts, even follows job titles built around them, such as Azure data engineer. Learn the ideas first, then the tools the adverts you want mention most. The tools change more often than the ideas.

How much do graduate and junior data engineers earn?#

In the six months to early September 2026, ITJobsWatch’s junior data engineer statistics showed a median advertised salary of £42,500 for UK permanent jobs, and its data engineer statistics showed £70,000 for jobs at all levels of experience. These are advertised salaries rather than what people are actually paid, so check each employer’s own figure.

To see data engineer pay by region, from official ONS figures, use our free Salary Checker.

What are the routes in?#

There are five main routes. Which one suits you depends on your degree, your savings and how soon you want to earn.

Graduate schemes. Some large employers take graduates into data engineering through technology or data schemes. TARGETjobs’ guide to data graduate jobs and schemes lists data engineer among the graduate roles in data, and notes that some schemes rotate you through areas such as analytics, engineering and governance before you specialise. It says most open in September and close between November and January, so apply early.

Start as a data analyst. An analyst job teaches you SQL, the business and how its data is used. It’s a recognised path towards engineering: dbt Labs, which surveys data teams each year, says in its State of Analytics Engineering 2025 summary that the analytics engineer role grew largely out of data analysts adopting good practice from software engineering. Our guide on how to become a graduate data analyst covers that first step.

The level 5 data engineer apprenticeship. In England, the data engineer apprenticeship is at level 5 and typically takes 24 months. GOV.UK explains how apprenticeships work: you’re employed and paid a wage while you train. Having a degree doesn’t rule you out: the funding rules allow an apprenticeship at the same or a lower level than a qualification you already hold, if it gives you substantive new skills and the training is materially different from your degree. Search for “data engineer” on GOV.UK’s Find an apprenticeship service.

Skills Bootcamps (England). Skills Bootcamps are free courses of up to 16 weeks for adults aged 19 or over, and the digital options include data engineering. When you finish, you’re offered a job interview with an employer. Check what each bootcamp covers and how much practical work it includes before you apply.

A master’s degree. The National Careers Service notes that graduates of other subjects can take a postgraduate conversion course to get into AI and data science. In England, the Master’s Loan is up to £13,206 for courses starting on or after 1 August 2026, towards your fees and living costs. Compare the cost and time with the other routes before you commit.

To compare the routes into data jobs, with their costs, time and funding, use our free Options Compared tool.

At Deeplink Coaching, data engineering comes after analysis. Students who pass the Data Analyst Launch Track can add our Data Engineering upgrade: about 160 hours of training in Python, SQL, Docker, Airflow, dbt and Spark, four data pipeline projects published on GitHub, and a second, unpaid digital internship brief in data engineering. It’s a paid programme, so weigh it against the free and funded routes above.

Which projects show you can do the work?#

A notebook that analyses a spreadsheet shows analyst skills. For engineering, show something that runs by itself, copes with problems and can be trusted: DfE’s job specification talks about the stability, robustness and resilience of the products you build. Two projects like these are a good start.

A scheduled pipeline from an API. The Carbon Intensity API, run by the National Energy System Operator, forecasts the carbon intensity of electricity in 14 regions of Great Britain for each half hour, more than 96 hours ahead. Write Python that collects the forecasts, loads them into a database and handles the same half hour arriving more than once as forecasts are updated. Then schedule it: GitHub Actions can run a workflow on a timetable, as often as every five minutes. Note that GitHub turns off scheduled workflows in a public repository after 60 days with no activity.

A batch pipeline on a large file. HM Land Registry’s Price Paid Data covers residential property sales in England and Wales from January 1995. The complete file is over 5 GB, so load it in chunks rather than all at once. Each month’s file marks records as added, changed or deleted, as HM Land Registry’s guide to the Price Paid Data explains, which is good practice for applying updates instead of reloading everything.

For either project:

1. Model the data. Turn the raw records into a few clean tables that an analyst could query, and draw a simple diagram of them.
2. Test it. Add checks that fail when something is wrong. dbt’s built-in data tests, for example, check for unique values, missing values, accepted values and relationships between tables.
3. Make it repeatable. Someone else should be able to run it from your instructions. Packaging it with Docker is a plus.
4. Write the README. Explain what the pipeline does, how the data flows, how it’s tested, what went wrong while you built it and what you would do next.

Our guide to building a data analyst portfolio explains how to present a project on GitHub, and has more ideas that use free UK data.

What happens in a data engineer interview?#

Expect the usual graduate stages (an application, perhaps online tests, then one or more interviews) with a technical element added. In the civil service, for example, the guide to Success Profiles says technical skills can be assessed through technical tests, exercises, presentations, work samples and interviews.

Prepare to talk through:

• SQL: joins, grouping, window functions and finding duplicates.
• Python: a short function that parses, deduplicates or merges records, and copes with empty or messy input.
• Data modelling: how you would design tables for a business process, and why.
• Pipeline design: how you would load new data every day, what happens if a run fails halfway or the same data arrives twice, and how you would know something had gone wrong.
• Your projects: why you chose each tool, what broke and how you fixed it.
• Behaviour questions: working with others, handling a mistake and explaining a technical problem to someone who isn’t technical.

Our guide to data analyst interviews and technical tests explains how technical stages and presentations usually work, and most of it applies to engineering roles too.