In financial analysis, source tables often contain missing values, duplicate records, mismatched keys, or conflicting transaction states. A strong answer should show that you can diagnose data quality issues before producing metrics from Ati reporting data.
You are given a dataset from Ati that appears incomplete or inconsistent across related tables. Explain how you would investigate the issue and structure your SQL workflow. Discuss how you would identify missing joins, unexpected NULLs, duplicate records, and conflicting values, and how you would decide whether to filter, impute, flag, or escalate those records.
Focus on a practical SQL-first approach rather than general data governance theory. The interviewer expects you to describe how you would validate row counts, compare aggregates across tables, use LEFT JOINs and CASE expressions to surface anomalies, and communicate assumptions before delivering a final dataset or metric.