Pinterest Data Manipulation (SQL/Python) interview questions at Pinterest concentrate on practical data wrangling and storytelling: expect tasks that test your ability to extract correct answers from messy datasets, write efficient SQL (joins, window functions, CTEs, aggregation) and produce clear, reproducible Python (pandas/NumPy) code. Interviewers evaluate correctness, performance, edge-case handling, clarity of thought, and how you communicate assumptions and trade-offs. What’s distinctive is the emphasis on product-relevant thinking—how your data work supports metrics, experiments, and scalable pipelines—so technical answers tied to real business context score higher. For interview preparation, focus on timed practice problems that mirror production scenarios: write SQL against sample event tables, optimize queries, and implement the same logic in pandas while showing tests and simple benchmarks. Practice explaining your approach aloud and documenting assumptions, and rehearse end-to-end workflows (data validation to final metric). Also be ready for a short coding assessment or technical screen followed by loop interviews that blend coding, domain questions, and behavioral discussion about impact and collaboration.