Data Engineer Interview Questions UK: 8 Real Questions and What Interviewers Want

Most Data Engineer interviews in the UK follow a pattern, and knowing it beats guessing. Below are eight questions from our bank of 25, each with a note on what the interviewer is really after.

Eight Data Engineer interview questions, and what the interviewer is thinking

1. What does a typical day as a data engineer involve?

Why they ask: Establishes whether you understand the role beyond buzzwords.

2. What is the difference between ETL and ELT, and which would you use on a modern cloud platform?

Why they ask: Foundational design question.

3. Explain the medallion architecture and how you would model data in it.

Why they ask: Common pattern in Databricks and Fabric projects.

4. What does it mean for a pipeline to be idempotent, and how do you achieve it?

Why they ask: Reliability design.

5. How would you design an incremental load from a source database?

Why they ask: Everyday pipeline design.

6. How do you orchestrate pipelines, and what makes a good Airflow DAG?

Why they ask: Orchestration is a core skill.

7. A nightly pipeline failed, and this morning's dashboards show wrong figures. What do you do?

Why they ask: Incident handling.

8. Tell me about a time you improved the performance or reliability of a data pipeline.

Why they ask: Evidence of impact.

A sample answer to question 1

What does a typical day as a data engineer involve?

A lot of my day is spent building and maintaining pipelines that move data from source systems into a warehouse or lakehouse, in a way that analysts and data scientists can trust. I usually start by checking orchestration alerts in Airflow or Azure Data Factory to see whether overnight loads succeeded and data quality tests passed. Then I work on development tasks, such as writing PySpark or SQL transformations, modelling tables, or adding a new source through an API or change data capture. I review pull requests, write tests and deploy through CI/CD. A fair amount of time goes on talking to analysts about what they need and to source system owners about schema changes. I also look at cost, performance and security. Reliability and good documentation are as important as new features.

Common mistake to avoid: Describing only coding; ignoring monitoring

Want the rest?

Full model answers, key points and common mistakes for all 25 questions are on the Data Engineer interview questions page. The first five are free, no sign-up needed.