Your question is Handling Missing Data in PostgreSQL. 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).
Missing data is common in banking and analytics workflows, including customer profiles, transaction feeds, and PNC performance reporting tables. Poor handling of NULLs can distort aggregates, break downstream logic, or hide data quality issues.
Explain how you handle missing data in a dataset using SQL, especially in PostgreSQL. You should discuss how you first identify missing values, when you would leave them as NULL, when you would replace them with defaults using functions like COALESCE, and when you would exclude incomplete rows from analysis. Also explain how your approach changes depending on whether the missing field is numeric, categorical, or date-based.
The interviewer is looking for a practical SQL-focused explanation rather than a machine learning answer. A strong response should cover both query behavior and data quality reasoning, with a few short PostgreSQL examples.