- Stages
- 5
- Typical timeline
- 3 to 5 weeks
- Difficulty
- Medium
- Format
- SQL and coding screen, then design and behavioural rounds
Data engineering interviews combine SQL, coding and data system design. Expect advanced SQL with joins, window functions and aggregation, Python coding for data transformation, and questions about data modelling such as star schemas and slowly changing dimensions. Design rounds ask you to build a pipeline for batch or streaming data, covering ingestion, storage, transformation, orchestration, data quality and cost. Interviewers look for practical experience with tools such as Spark, Airflow, dbt or cloud warehouses, but care more about fundamentals: idempotent pipelines, handling late or bad data, and making data trustworthy for the people who use it.
The interview process, stage by stage
- Recruiter screenTools, pipelines you have built and data scale.
- SQL and codingComplex SQL and Python data manipulation.
- Data modellingDesign tables for analytics use cases.
- Pipeline designDesign a batch or streaming pipeline end to end.
- BehaviouralWorking with analysts, data quality incidents and ownership.
How you're scored
Writes correct, efficient queries and transformations.
Designs tables that suit how data is used.
Builds reliable, idempotent and observable pipelines.
Understands what analysts and the business need from data.
Likely Data Engineer interview questions
Write a query to find each customer's most recent order.
A strong answer includes: Use a window function such as row_number partitioned by customer and ordered by date descending, then filter to the first row, and mention ties.
Design a pipeline that loads daily sales data from several stores into a warehouse.
A strong answer includes: Ingestion with schema checks, a raw layer, transformations into modelled tables, idempotent daily runs, orchestration with retries, data quality tests and alerts.
What is a slowly changing dimension and how do you handle it?
A strong answer includes: Attributes that change over time, such as a customer's address. Explain type 1 overwrite and type 2 history with valid-from and valid-to dates, and when to use each.
Every question for this interview, with what strong answers include. Free with an account for the first few; all of them with Pro.
Common mistakes
- Pipelines that can't be safely re-run
- Ignoring late-arriving or duplicate data
- No data quality checks
- Designing for huge scale when the data is small
Questions to ask them
- What does the data stack look like today?
- Where do data quality problems usually come from?
- Who are the main users of the data platform?
- How are pipeline failures detected and handled?
Candidate experiences
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Share your interviewFrequently asked questions
What is asked in a data engineer interview?
Advanced SQL, Python coding for data transformation, data modelling questions such as designing a star schema, and a pipeline design round covering ingestion, transformation, orchestration, data quality and monitoring.
What SQL topics should data engineers know?
Joins of all types, aggregation, window functions such as row_number and lag, common table expressions, deduplication, handling nulls and query performance basics like indexes and partitioning.
How do I approach a pipeline design question?
Clarify sources, volume, freshness and consumers, then cover ingestion, storage layers, transformation, orchestration, idempotency, data quality checks, monitoring and cost. Explain batch versus streaming choices.
Do I need Spark experience for data engineering interviews?
It helps for big data roles, but many teams use cloud warehouses and dbt instead. Understanding distributed processing concepts such as partitioning and shuffles is more important than any one tool.
