The Data Engineering Interview Playbook

Data engineering interviews vary widely by company, but most follow a pattern. Here's what to expect at each stage and how to prepare effectively.

SQL Round

Expect 2-3 medium-to-hard SQL problems. Companies test window functions, self-joins, recursive CTEs, and performance optimisation. Practice on LeetCode (medium difficulty) and StrataScratch. Pro tip: always explain your approach verbally before writing code — interviewers want to see your thought process.

Python Round

You'll be asked to solve data manipulation problems — reading files, cleaning data, implementing basic algorithms. Focus on Pandas operations, error handling, and writing efficient loops. The key isn't perfect syntax but demonstrating that you can write production-quality code.

System Design

This is where senior roles differentiate themselves. You'll be asked to design a data pipeline end-to-end. Use the STAR framework: Scenario, Tradeoffs, Architecture, Result. Discuss data sources, ingestion methods, storage choices, transformation layers, orchestration, monitoring, and alerting. Be prepared to justify every decision with tradeoffs.

Behavioural

Stories about debugging a production issue at 2 AM, convincing stakeholders to adopt better data practices, or mentoring junior engineers. Use the STAR method (Situation, Task, Action, Result) to structure each answer.

Questions to Ask Them

• "How do you handle data quality? What's your alerting stack?"
• "What's the on-call rotation like for data pipelines?"
• "How is the data team structured? Do engineers work closely with analysts?"
• "What's your data stack today, and what would you like it to be in a year?"