In financial analysis, source data is often incomplete, duplicated, delayed, or inconsistent across operational systems. Interviewers want to know how you make your SQL analysis trustworthy before presenting conclusions.
Explain how you ensure your analysis is accurate when the data is incomplete or messy. In your answer, describe how you would validate joins, handle missing values, detect duplicates or conflicting records, and use SQL to flag questionable data rather than silently dropping it. You should also explain how you would communicate assumptions and quantify the impact of data quality issues on the final result.
Focus on a practical SQL-first workflow appropriate for a medium-difficulty interview. You do not need to design a full data platform, but you should show how you would inspect data quality, structure cleaning logic, and preserve auditability in a finance setting such as portfolio, payment, or recovery reporting.