Google Data Manipulation (SQL/Python) interview questions are a common hurdle across Google roles that work with product metrics, experimentation, and large datasets. What’s distinctive about Google interviews is the expectation that candidates can combine crisp SQL for set-based aggregation with pragmatic Python for row-level transformations or complex calculations. Interviewers evaluate correctness and clarity of thought, ability to reason about edge cases and NULLs, query performance intuition (joins, indexing, CTEs), and practical tradeoffs when moving work between SQL and Python. You should also expect live whiteboard or take-home exercises that mimic real data problems rather than abstract puzzles. Effective interview preparation focuses on deliberate practice: write and optimize queries on realistic schemas, translate SQL outputs into concise Python data-frame transformations, and time-box solutions while explaining assumptions. Practice explaining why you chose a window function versus a GROUP BY, or when to push calculations into SQL for performance. Prepare short narratives about past data work that highlight debugging, validation, and measurable impact. Ahead of the interview, review common pitfalls such as off-by-one time windows, incorrect NULL handling, and misinterpreted JOIN semantics so your solutions are both correct and production-minded.