Meta Data Manipulation (SQL/Python) interview questions are a central part of Meta’s hiring for data scientist, data engineer, and analytics roles and usually emphasize practical, product-focused problem solving over abstract algorithm puzzles. What’s distinctive is the scale and product context: interview problems mirror real-world analytics tasks with messy data, session/event tables, and metrics design. Interviewers evaluate accuracy, clarity, and maintainability of your SQL or pandas code, your handling of edge cases (NULLs, deduplication, sampling), and your ability to explain trade-offs between readability and performance using CTEs, window functions, joins, and vectorized Python operations. For interview preparation, expect a timed technical screen (often using a shared editor) with SQL and Python data-manipulation tasks, followed by deeper loop rounds combining coding, product-metrics reasoning, and behavioral questions. Practice end-to-end problems: translate a product question into concrete metrics, write and optimize queries or pandas pipelines, narrate assumptions, and validate results. Work timed problems in CoderPad-like environments, rehearse clarifying questions, and review common pitfalls such as filter vs HAVING, NULL behavior, and inefficient joins. Regular mock interviews and focused drills on window functions, groupings, merges, and missing-data strategies will give the confidence and fluency Meta typically looks for.