Data Engineer
What the work looks like, the stacks behind the title, what each level means and the training that helps, then how interviews run and how to prepare for each step.
What a data engineer actually does
An engineer who builds the pipelines and platforms that move, clean and store the data that analysts, products and AI teams rely on.
A data engineer builds and runs the pipelines that collect data from many sources, transform it and store it in a data platform. Analysts, data scientists, ML teams and products then use this data every day.
The work covers batch pipelines, streaming pipelines and the data platform itself. Data engineers choose how data is stored and modeled, test its quality, document it and control who can access it.
They also watch the cost and the performance of queries and storage, which grow fast with the volume of data. More and more teams also work with AI coding tools such as Claude Code, Cursor or GitHub Copilot, and expect engineers to use them with judgment.
- Building or changing a pipeline for a new data source
- Fixing a failed job or a data quality issue
- A review of query and storage costs
- A design discussion with analysts or ML teams on a new dataset
- Code reviews, tests and documentation
The same title, very different stacks
Most data engineering searches are defined first by the type of pipelines the team runs, then by the ground every data engineer shares.
What each level means in practice
Titles vary from one company to the next. What changes from one level to the next is the scope you own and the decisions you take.
- Works on pipelines designed by a senior engineer
- Learns the data platform, the sources and the tools
- Writes tested transformations with code review
- Builds and runs pipelines on their own
- Fixes data quality issues and failed jobs
- Works directly with analysts and data scientists
- Designs pipelines and data models for a domain
- Takes choices on cost, performance and quality
- Mentors other engineers and leads code reviews
- Sets the architecture of the data platform
- Leads choices across teams on tools and standards
- Writes the design documents others build on
The paths into the role, and what really counts
Companies look first at pipelines you have built and run in production. Education and certifications help most early in a career, or to change field.
- Engineering school or master's in computer scienceThe most common path in France, and often expected by large companies.
- University degree in computer scienceThe most common path in the US, at bachelor's or master's level.
- Backend engineering firstMany data engineers start as backend engineers before moving to data.
- Data analysis or BI firstData analysts and BI developers with strong SQL and coding skills also move into the role.
Our view: for this role, a pipeline you built and can walk through, from the sources to the people who use the data, often says more than any certificate.
A conversation with a senior tech recruiter
Your first interview is with one of our senior tech recruiters, who knows the role, the stacks and the levels in this guide. The conversation covers your own work through real examples, and the technical exercises come later, with the company. Before the call, we send you the six themes we will cover so you can prepare your examples.
- What you owned yourselfThe pipelines, data models or platforms you were responsible for, and your own part in the team's work.
- Your environment and its constraintsData volume, latency, sources, budget or rules on data access: what shaped your technical choices.
- The decisions you madeBatch or streaming, which tools and which data model, the options you compared and the reasons for your choice.
- The impact of your workWhat changed thanks to your work, with numbers when you have them: volume, freshness, costs or incidents.
- How you work with other teamsYour work with analysts, data scientists, ML teams and product teams, and how you settle a disagreement.
- What you want nextThe kind of data, platform and scope you are looking for in your next role.
- Pipelines in production and their volume
- Data quality and testing
- Cost and performance choices
- Work with analysts and ML teams
The same ground for every candidate. For a given role, every candidate answers the same final questions, built with the hiring manager around what the role really needs.
Ready for the next steps. After our call, we tell you how the company runs its interviews, who you will meet at each step and what each step is meant to assess.
What the company will assess, step by step
Each company runs its own process, and we walk you through its steps before you start. Data engineering processes often combine the steps below, and the depth expected at each step grows with your level.
- A SQL exerciseLive queries on a realistic dataset, often with joins, window functions and data that needs cleaning.
- A coding exerciseLive coding or a take-home in Python, usually a data transformation or a small pipeline with tests.
- A data system designA pipeline to design from source to consumer: batch or streaming, storage, data model, quality checks and cost.
- Meeting the manager and the teamHow you work, how you take decisions with others and what the team expects in your first months.
- ConfirmedYou solve the exercise cleanly, test your results and explain each choice in simple words.
- SeniorYou lead the design, weigh cost, latency and quality, and point out risks and limits before you are asked.
- Staff and PrincipalYou run most of the interview as a peer, think about the whole data platform and connect the design to the needs of several teams.
How to prepare as a data engineer
Practical advice for each step of a data engineering process, from the way you introduce yourself to the questions you ask at the end of each interview.
Start with the pipelines and platforms you worked on, their volume and your part in them. Mention your stack in one sentence, then close with what you want next.
For each one, prepare the sources, the volume, your decision, the options you set aside, the result and what you would change today. Say "we" for the team and "I" for your own part.
SQL exercises are common at every level. Practise joins, window functions and data cleaning, and explain your query while you write it.
In a data system design, ask about the sources, the volume, the freshness needed and the users of the data before you draw anything.
Interviewers often ask about scale. Prepare the volume, the freshness, the cost and the incident rate of the pipelines you ran.
Rules on AI coding assistants differ from one company to the next, and sometimes from one step to the next. Ask before each step, and when they are allowed, explain what you ask the tool and how you check its output.
- Walk us through a pipeline you built, from the source to the people who use the data.
- How do you handle late or duplicate data?
- Batch or streaming: how do you choose?
- How do you test the quality of data in production?
- Which data platform and orchestrator does the team use?
- Who uses the data: analysts, ML teams or products?
- How does the team test data quality and handle incidents?
- Who is on call for the pipelines, and how often?
- How does the team follow query and storage costs?
- How does the team use AI coding tools day to day?
Where data engineers come from, and where they go
The roles people most often hold before this one, and the ones they most often move to.
Many data engineers also start as data analysts or BI developers, and some move to data architect roles.