SQL Data Quality Quant Interview Guide
SQL data quality quant interview guide for row counts, nulls, duplicates, keys, ranges, timestamp checks, validation, and examples.
Candidates validating query outputs and joined datasets.
Data quality checks make SQL trustworthy
A query result should be checked for row count, duplicate keys, unexpected nulls, value ranges, and timestamp validity before being trusted.
Check grain before metrics
If the output grain is wrong, every downstream metric can be wrong. Validate one row per intended key before aggregating or reporting.
Concrete example
After joining trades to prices, compare trade count before and after the join and inspect unmatched trades or duplicate matches.
Timestamp checks catch leakage
For time-sensitive data, ensure joined rows were available at the event time. Future timestamps can create inflated results.
Common mistakes
Candidates often assume clean input. In quant data work, validation is not optional because silent data errors look like real signals.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.