Python questions from Goldman Sachs data engineering interviews.
These python questions are sourced from Goldman Sachs data engineering interviews. Each includes an expert-level answer. This set leans toward senior-level depth (4 of 5 are tagged hard). Recurring themes are python, sql, and partition — 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 5 curated questions: 1 easy, and 4 hard. The distribution skews toward harder problems, reflecting the depth expected in senior-level interviews.
The most frequently tested areas in this set are python (3), sql (3), partition (2), and spark (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. 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.
How would you handle memory constraints when processing a large dataset in Python?
How would you process a 10TB dataset on a single machine in Python?
Implement a recursive algorithm to find the nth Fibonacci number.
Write a Python script to parse a large JSON file, filter records based on a condition, and write the result to a database.
Write code to merge two sorted arrays without using extra space.
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