General questions from Goldman Sachs data engineering interviews.
These general questions are sourced from Goldman Sachs data engineering interviews. Each includes an expert-level answer. This set leans toward the medium-difficulty band most real interviews actually live in (3 of 7). Recurring themes are sql, join, and spark — these patterns appear most often in real interviews and reward the deepest preparation. Average answer is around 1 minute of reading — plan roughly 1 hour to work through the full set thoughtfully.
This collection contains 7 curated questions: 2 easy, 3 medium, and 2 hard. The balanced mix of difficulties makes this set suitable for engineers at any career stage.
The most frequently tested areas in this set are sql (2), join (2), spark (1), window (1), partition (1), and etl (1). Focusing on these topics will give you the highest return on your preparation time.
Start with the easy questions to warm up and solidify fundamentals. Medium-difficulty questions form the bulk of real interviews — spend the most time here and practice explaining your reasoning out loud. Hard questions often appear in senior and staff-level rounds; attempt them after you're comfortable with the basics. For each question, try answering before revealing the solution. Use our AI Mock Interview to simulate real interview conditions and get instant feedback on your responses.
Compare batch processing and stream processing for financial data.
Compute the moving average of daily transactions over a 7-day window.
Describe a time when you had to deal with a major data quality issue. How did you handle it?
Describe the concept of data sharding and when to use it.
Explain how you ensure data security and compliance in sensitive data projects.
How do you prioritize competing demands in a high-pressure environment?
How would you model hierarchical data in a relational database?
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