Amazon Data Manipulation (SQL/Python) interview questions at Amazon focus on practical problem solving with real-world data: efficient joins and window functions in SQL, group and aggregate logic, NULL-handling and performance tradeoffs, and equivalent Pandas/NumPy patterns for in-memory work. What’s distinctive is the mix of product-sense and scale-awareness — interviewers evaluate both correctness and how your solution would behave on large tables or within a production pipeline. Expect timed live-coding or take-home tasks where clarity of assumptions, test cases, and incremental refinement matter as much as final syntax. For interview preparation, prioritize translating tasks between SQL and Pandas, practicing common subproblems (deduplication, rolling/window calculations, CTE refactors, and merge strategies), and explaining complexity and scalability tradeoffs. Be ready to justify index or partition choices, to optimize a slow query, and to narrate decisions with Amazon’s customer-and-ownership lens. Practicing with real datasets and mock interviews that simulate pressure will improve fluency and the concise communication Amazon looks for.