Interview
Questions
Databricks: Customer Architecture and Production Readiness
Practice customer architecture trade-offs, governed data, technical proofs, and credible commitments from prototype to production. Independent spoken preparation, not a replacement for coding or a verified question bank.
Practice these questions directly. An editable copy is yours to change; the original stays unchanged.
The data engineering ecosystem has many tools and frameworks. How do you evaluate and choose between different technologies for a project? Give a specific example.
How do you decide between batch and streaming processing for a given use case? Describe a situation where you made this architectural decision.
Data privacy and compliance are increasingly important. Tell me about a time you had to consider privacy requirements (GDPR, PII handling, etc.) in your data engineering work.
Data infrastructure can be expensive. Tell me about a time you optimized data pipelines or storage for cost. What approaches did you take?
For a customer's proposed lakehouse migration, how would you turn their performance, access, and cost requirements into a small proof of concept with clear pass or fail criteria?
A customer prototype works with a small dataset, and Sales wants to call it production-ready. What evidence would you require before making that commitment?
Data engineering work often impacts business decisions. Describe a time you explained technical data concepts or limitations to non-technical stakeholders. How did you ensure understanding?