Data Engineer interview questions & mock practice
A Data Engineer interview in 2026 runs across 4 rounds — sql & data modelling, big data & pipelines, coding round, system design. Below are the most-asked Data Engineer interview questions and a focused prep plan. Rehearse every answer with OnJob's free AI mock interview and get instant, specific feedback before the real one.
The Data Engineer interview process
Data pipelines, SQL, big-data tools and warehousing — the data-engineering interview for analytics, fintech and product teams building data platforms.
SQL & data modelling
Complex queries, window functions, star vs snowflake schema design.
Big data & pipelines
Spark, Hadoop, ETL/ELT design and batch vs streaming.
Coding round
Python/Scala data manipulation and DSA basics.
System design
Design an end-to-end data pipeline or warehouse.
Most-asked Data Engineer interview questions
12 of the questions Data Engineer candidates are asked most often in India. Practise answering each one out loud in your AI mock interview.
- 1. What is the difference between ETL and ELT, and when do you use each?
- 2. Explain the difference between a data warehouse, a data lake and a data mart.
- 3. What is the difference between a star schema and a snowflake schema?
- 4. How does Apache Spark work, and what is an RDD vs a DataFrame?
- 5. Explain the difference between batch processing and stream processing.
- 6. What is partitioning and bucketing in Hive, and why do they matter?
- 7. How do you handle slowly changing dimensions (SCD types 1, 2 and 3)?
- 8. What is data normalisation, and when would you denormalise for analytics?
- 9. How would you design a pipeline to ingest and process millions of events a day?
- 10. What is the difference between OLTP and OLAP systems?
- 11. How do you ensure data quality and handle duplicate or late-arriving data?
- 12. Explain how you'd optimise a slow Spark job.
How to prepare for your Data Engineer interview
Be fluent in advanced SQL: window functions, CTEs, query optimisation and reading execution plans.
Know Spark hands-on — RDDs vs DataFrames, transformations vs actions, partitioning and shuffles.
Understand warehousing concepts cold: dimensional modelling, SCDs, fact vs dimension tables and star schemas.
Be ready to design an end-to-end pipeline (source → ingest → transform → store → serve) and justify each tool.
Brush up on at least one orchestrator (Airflow) and one cloud warehouse (Snowflake, BigQuery or Redshift).
Practise other roles
- Software Engineer
- Frontend Developer
- Backend Developer
- Full Stack Developer
- Data Analyst
- Data Scientist
- Product Manager
- DevOps Engineer
- Java Developer
- Python Developer
- UI/UX Designer
- Sales / Business Development
- Digital Marketing
- HR / Recruiter
- Accountant
- Customer Support
- Machine Learning Engineer
- QA / Test Engineer
- Android Developer
- iOS Developer
- Business Analyst
- Project Manager
- Scrum Master
- SQL Developer
- React Developer
- Node.js Developer
- Cloud Engineer (AWS)
- Cybersecurity Analyst
- Network Engineer
- Database Administrator
- SEO Specialist
- Content Writer
- Graphic Designer
- Sales Executive
- Business Development Manager
- Operations Manager
- Financial Analyst
- Chartered Accountant
- Customer Success Manager
- Technical Support Engineer
- Civil Engineer
Interview prep guides
Data Engineer interview — FAQs
What questions are asked in a Data Engineer interview?
Common Data Engineer interview questions include: What is the difference between ETL and ELT, and when do you use each? Explain the difference between a data warehouse, a data lake and a data mart. What is the difference between a star schema and a snowflake schema? How does Apache Spark work, and what is an RDD vs a DataFrame? Interviews usually run across 4 rounds — SQL & data modelling, Big data & pipelines, Coding round, System design. Practice all of them with instant AI feedback using OnJob's free mock interview.
How many rounds are in a Data Engineer interview?
A typical Data Engineer interview has 4 rounds: SQL & data modelling (Complex queries, window functions, star vs snowflake schema design.); Big data & pipelines (Spark, Hadoop, ETL/ELT design and batch vs streaming.); Coding round (Python/Scala data manipulation and DSA basics.); System design (Design an end-to-end data pipeline or warehouse.).
How do I prepare for a Data Engineer interview?
To prepare for a Data Engineer interview: Be fluent in advanced SQL: window functions, CTEs, query optimisation and reading execution plans. Know Spark hands-on — RDDs vs DataFrames, transformations vs actions, partitioning and shuffles. Understand warehousing concepts cold: dimensional modelling, SCDs, fact vs dimension tables and star schemas. Then run a full AI mock interview on OnJob to rehearse out loud and get instant, specific feedback before the real thing.
What skills do I need for a Data Engineer role?
Core Data Engineer skills tested in interviews include SQL, Apache Spark, Python, Airflow, Data Warehousing, ETL, Kafka. OnJob shows you exactly which of these skills stand between you and a 100% match on every live Data Engineer job.
Is OnJob's Data Engineer mock interview free?
Yes. OnJob's AI mock interview is free to start (₹0) and gives you instant feedback on your answers. Pro (₹99/month) adds unlimited interview-prep AI alongside recruiter tracking and unlimited applications.
Ace your Data Engineer interview
Rehearse every Data Engineer question out loud with OnJob's AI mock interview and get instant, specific feedback. Then apply to AI-matched jobs in one click — free to start.
Everything about Data Engineer on OnJob
Move across the whole Data Engineer topic — live openings, real salary data, the job description, interview prep, and early-career routes — all in one place.