Robinhood data engineering interview questions are bilingual — SQL and Python in roughly equal measure — with a fintech-correctness edge that most generic interview-prep posts miss. Four primitives carry the loop: dict.get(s, 0) + 1 hash-table counters that aggregate stock-purchase events by symbol, INNER JOIN trades + users + GROUP BY + ORDER BY count DESC LIMIT N for top-N city / member-transfer rankings, LAG(volume) OVER (PARTITION BY stock_symbol ORDER BY trade_date) for day-over-day volume or balance change, and GROUP BY user_id HAVING SUM(notional) > limit for end-of-day threshold and notional-cap checks. The framings are everyday brokerage data engineering — count purchases per ticker, surface the top cities by completed trades, compute a daily volume percentage change, flag any account whose option exposure crosses a regulatory limit.…