Your question is Handling Missing Customer Data. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
Large customer datasets often contain incomplete fields such as missing country, birth date, KYC status, or marketing consent. In a financial app context, poor handling of missing values can distort reporting, segmentation, and downstream decision-making.
You are asked to explain how you would handle missing data in a large customer dataset stored in PostgreSQL. Describe how you would identify missing values, distinguish between truly unknown data and invalid placeholders, decide when to impute versus exclude records, and preserve data quality for analysis in surfaces such as Revolut customer reporting or CRM segmentation.
The interviewer expects a practical SQL-focused answer rather than a purely statistical one. You should discuss profiling queries, NULL handling, validation rules, and how you would structure the logic so analysts can reuse it consistently at scale.