How to become a data engineer

Data engineer in a nutshell

• Builds and maintains the pipelines that move data from source to usable form.

• Makes data reliable, well-structured and available for analysts, data scientists and AI.

• Earns a UK median salary of around £70,000, rising to roughly £90,000 for lead roles.

• In demand wherever AI and analytics are scaling, from finance and healthcare to government and retail.

• Requires no single qualifying degree, though strong programming and database skills help.

• Works at the foundation of the data team, enabling analytics, data science and AI.

What does a data engineer do?

A data engineer builds the infrastructure that turns raw, scattered data into something a business can use. While a data scientist analyses data, a data engineer makes sure the right data arrives in the right shape, on time.

Day to day, that means designing data pipelines: automated processes that extract data from source systems, transform it and load it into a warehouse or lake. They model how data is structured, keep it clean and reliable at scale, and increasingly do so in the cloud on AWS, Azure or Google Cloud. The focus is on performance, security and quality, so everyone downstream can trust what they use.

Why do businesses need data engineers?

Data is only valuable if it can be trusted and accessed. Data engineers build the foundations that make that possible; without them, analytics and AI projects stall.

The AI boom has made this urgent. In a 2024 Gartner survey, 63 per cent of organisations were not confident their data was ready for AI, and Gartner expects most AI projects to be abandoned for that reason. Reliable models, real-time analytics and automation all depend on clean, well-governed pipelines someone has to design and run.

For businesses, the value is twofold: data engineers make reporting faster and more trustworthy, and unlock advanced use cases such as personalisation, fraud detection and machine learning. Many now also train software engineers and analysts into data engineering rather than compete for scarce hires.

The path to becoming a data engineer

There is no single route into data engineering, though most people arrive with some coding or data experience. Common entry points include software engineering, data analysis, database administration and business intelligence.

If you are wondering how to become a data engineer, three things matter most. Start by getting fluent in SQL and Python, the everyday tools. Next, learn how data moves through an organisation: building pipelines, modelling data and working with a cloud platform and a warehouse or lake. Then build a portfolio: a pipeline you have taken from source to dashboard says more than any certificate.

Structured training speeds this up. Recognised data engineering, cloud and database qualifications give career changers a clear path and signal credibility. QA’s data engineer training can help you find the right route, whether you are entering the field or upskilling a team.

The top skills every data engineer needs

Data engineering blends software engineering discipline with deep data knowledge. The core skills include:

• SQL and databases: the bedrock of storing, querying and shaping data.

• Programming: fluency in Python, with Scala or Java useful for large-scale processing.

• Data pipelines and ETL: designing automated flows that move and transform data.

• Cloud platforms: building on AWS, Azure or Google Cloud, where most modern data lives.

• Data modelling and warehousing: structuring data so it is efficient and easy to use.

• Data quality and governance: keeping data accurate, secure and compliant.

AI fluency now matters too. As machine learning and generative AI move into production, data engineers build the governed pipelines those models depend on, putting the role at the centre of most AI strategies. Developing these skills is where structured training earns its place, data engineering training support teams doing exactly that.