Context

In Capital One analytics workflows, especially when working with transaction, customer, or credit performance data in Snowflake- or PostgreSQL-backed reporting layers, missing values can materially change downstream metrics. Interviewers ask this to assess whether you can distinguish between data cleaning, business logic, and metric integrity.

Core Question

Explain how you would handle a dataset with missing values using SQL. Your answer should cover:

1. How you identify missing data (NULLs, blanks, placeholder values)
2. When to filter rows out versus impute values
3. How functions like COALESCE and CASE WHEN help in analysis
4. Risks of handling missing values incorrectly in aggregates and reporting

Scope Guidance

Keep the discussion practical and SQL-focused. The interviewer is not looking for advanced statistical imputation; they want a clear framework for profiling missingness, choosing an appropriate treatment based on business meaning, and implementing that logic safely in PostgreSQL.