Roblox Data Manipulation (SQL/Python) interview questions focus on working with large, event-driven game datasets where time-series logic, sessionization, and performance matter as much as correctness. Interviewers typically evaluate your ability to write clear, efficient SQL (joins, window functions, CTEs, aggregations and NULL handling) and to transform and validate data with Python (pandas, vectorized operations, memory-aware processing and readable code). Expect questions that probe tradeoffs between correctness and latency, how you reason about edge cases in user/session data, and your ability to communicate assumptions and validation steps. For interview preparation, practice end-to-end problems: derive metrics from raw event logs, debug surprising aggregations, and optimize queries for scale while keeping results reproducible. Prepare for a mix of live SQL/pair-programming, take‑home exercises, and product-facing discussions about metric definitions. Emphasize clear, testable code, articulate assumptions, and rehearse concise explanations of tradeoffs — that combination is often the differentiator in Roblox technical interviews.