Handling Missing Data in PostgreSQL

Context

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.

Core question

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.

Scope guidance

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.