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

Most Azure Data Engineer interviews in the UK follow a pattern, and knowing it beats guessing. Below are eight questions from our bank of 16, each with a note on what the interviewer is really after.

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

1. What are the key differences between Azure Data Factory, Synapse pipelines and Microsoft Fabric Data Factory, and how do you choose between them?

Why they ask: Azure data tooling has shifted towards Fabric, so interviewers test whether you understand the landscape.

2. Describe how you would design a medallion architecture on Azure using Data Lake Storage Gen2 and Delta Lake.

Why they ask: Medallion design is the standard lakehouse pattern in Azure data engineering.

3. How would you build an incremental load pipeline in Azure Data Factory from an on-premises SQL Server to a lakehouse?

Why they ask: Incremental loading with hybrid connectivity is a classic real-world task.

4. How do you secure an Azure data platform, covering networking, identity, secrets and data protection?

Why they ask: Security is critical, especially for UK public sector and regulated clients.

5. What is the difference between Azure Synapse dedicated SQL pools, serverless SQL and Azure Databricks, and when would you use each?

Why they ask: Choosing the right compute is central to cost-effective design.

6. A Data Factory pipeline that runs every night has failed, and the reports are blank at 7am. How do you respond?

Why they ask: Production support is a core day-to-day responsibility.

7. Tell me about a time you designed or delivered a data platform or pipeline on Azure from requirements to production.

Why they ask: To assess real end-to-end delivery experience.

8. What do you enjoy about Azure data engineering, and how do you keep up with Microsoft's rapidly changing data platform?

Why they ask: To gauge enthusiasm and your approach to continuous learning in a fast-moving stack.

A sample answer to question 1

What are the key differences between Azure Data Factory, Synapse pipelines and Microsoft Fabric Data Factory, and how do you choose between them?

Azure Data Factory is the standalone, mature orchestration and data movement service, with pipelines, mapping data flows and a large connector library. Synapse pipelines are essentially the same engine embedded in Synapse Analytics workspaces, alongside dedicated SQL pools and Spark. Fabric Data Factory is the newer, SaaS version within Microsoft Fabric, with Dataflows Gen2, pipelines and tight integration with OneLake, Lakehouses and Warehouses, billed through capacity. For existing, production workloads with specific connectors, private networking or self-hosted integration runtimes, ADF remains a safe choice. For greenfield analytics where the team wants unified storage, Power BI integration, simplified governance and Direct Lake, I would lean towards Fabric, noting feature gaps such as some connectors and managed virtual network requirements that I would check against current documentation. I would consider cost model, team skills, security and compliance needs, and migration effort, and avoid dogmatism, since many organisations will run ADF and Fabric side by side for a while.

Common mistake to avoid: Assuming Fabric replaces everything today; ignoring migration effort

Want the rest?

Full model answers, key points and common mistakes for all 16 questions are on the Azure Data Engineer interview questions page. The first five are free, no sign-up needed.