Your question is Quality Check Process for Large Datasets. 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).
Large datasets in platforms like Veeva Vault often feed operational reporting, customer workflows, and downstream analytics, so a weak quality check can create misleading results at scale.
Walk me through how you would perform a quality check on a large dataset using SQL. Explain how you would structure your checks for completeness, duplicates, nulls, invalid values, and reconciliation against related tables. Describe how you would use SQL patterns such as joins, aggregations, and CTEs to make the process repeatable and easy to review.
Keep the answer practical and analyst-focused. The interviewer is looking for a clear framework: how you define expectations, write validation queries, investigate anomalies, and communicate findings before the data is used in reporting or analysis.