Databricks Data Engineer Interview Questions (2026)

Landing a Data Engineer role at Databricks requires targeted preparation. Databricks interviews include coding rounds, system design sessions, and domain-specific discussions around data platforms. Engineering candidates face questions about distributed computing, Apache Spark internals, and lakehouse architecture. The company values technical depth, open-source contributions, and the ability to simplify complex data challenges for users. This guide covers the most frequently asked questions and insider tips to help you succeed in your Databricks Data Engineer interview.

About the Databricks Interview Process

Databricks interviews assess deep expertise in data engineering, distributed systems, and passion for democratizing data and AI.

Databricks interviews include coding rounds, system design sessions, and domain-specific discussions around data platforms. Engineering candidates face questions about distributed computing, Apache Spark internals, and lakehouse architecture. The company values technical depth, open-source contributions, and the ability to simplify complex data challenges for users.

Why Databricks Data Engineer Interviews Are Different

Databricks Data Engineer interviews differ from standard Data Engineer interviews in several key ways. The company has a unique interview culture, specific evaluation criteria, and expects candidates to demonstrate alignment with their values and mission. Understanding these differences gives you a significant advantage over other candidates.

Top 10 Data Engineer Interview Questions at Databricks

1. At Databricks, you might be asked: Explain the difference between ETL and ELT.
2. Databricks candidates should prepare for: How would you design a data pipeline for real-time analytics?
3. Databricks candidates should prepare for: What is the difference between a data lake and a data warehouse?
4. Databricks candidates should prepare for: Describe your experience with Apache Spark or similar frameworks.
5. Databricks interviewers often ask: How do you handle data quality and validation?
6. At Databricks, you might be asked: What is data partitioning and why is it important?
7. At Databricks, you might be asked: How do you optimize query performance on large datasets?
8. Databricks interviewers often ask: Describe a complex data pipeline you have built.
9. Expect this at Databricks: How do you handle schema evolution in data pipelines?
10. Databricks candidates should prepare for: What tools do you use for data orchestration?

Databricks-Specific Preparation Tips for Data Engineer Candidates

• Study Apache Spark architecture, distributed computing, and lakehouse concepts
• Prepare for system design questions involving data pipelines and analytics platforms
• Practice coding problems with focus on data manipulation and distributed algorithms
• Research the lakehouse paradigm and how it unifies data warehousing and data lakes
• Show knowledge of MLOps, data governance, and modern data stack trends

General Data Engineer Interview Tips

• Be proficient in SQL and at least one programming language
• Understand distributed computing concepts
• Know common data modeling techniques
• Be ready to discuss data governance and compliance

Preparation Timeline for Databricks Data Engineer Interviews

• 4 weeks before: Research Databricks culture, recent news, and the specific team you are applying to.
• 2-3 weeks before: Practice technical questions daily and prepare behavioral stories using the STAR method.
• 1 week before: Do full mock interviews with HireFlow AI simulating Databricks interview style.
• Day before: Review your notes, prepare questions for the interviewer, and get a good night of rest.

Practice Databricks Data Engineer Interview with HireFlow AI — our AI adapts to Databricks's interview style and gives real-time feedback.