PWC Data Engineer Interview Questions & Answers (2026)

PWC is known for rigorous data engineering interviews that focus on practical, production-level knowledge. With 41 questions in our vault, the most common category is Spark/Big Data (26 questions). Difficulty breakdown: 12 easy, 11 medium, 18 hard. Expect system design and optimization questions at senior levels.

- **Q1**: Design a cost-aware resource strategy for a Databricks workload with spiky and batch jobs. Explain Dynamic Resource Allocation, when to disable it, and how min/max executors and spot instances affect cost and SLAs. - **Q2**: Explain how Adaptive Query Execution changes the economics of Spark tuning. What problems does it solve at runtime, and when might you still need manual intervention (e.g., salting, broadcast hints)? - **Q3**: What challenges do you face when managing multiple notebooks in Git? - **Q4**: What are the differences between Azure Key Vault-backed and Databricks-backed Secret Scopes? - **Q5**: What is Secret Scope, and how is it used in Databricks?

- **Spark/Big Data**: 26 questions - **System Design/Architecture**: 4 questions - **SQL**: 4 questions - **General/Other**: 3 questions - **Cloud/Tools**: 2 questions - **Behavioral**: 1 questions - **Python/Coding**: 1 questions

Focus on Spark/Big Data questions first, as they dominate PWC's interview pattern. Practice the top-frequency questions below, then move to adjacent categories. For senior roles, expect 1-2 system design rounds.