Your question is Cleaning Messy Campaign Data. 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).
Messy marketing data can distort campaign reporting, attribution, and downstream analysis. Interviewers want to see that you can turn unreliable raw data into a trustworthy analysis-ready table using a clear SQL workflow.
You are asked to explain how you would clean and structure a messy dataset before starting analysis, using PostgreSQL and SQL-first thinking. Describe how you would inspect the data, identify missing values, standardize inconsistent fields, handle duplicates, validate business rules, and create a final dataset suitable for reporting in a marketing analytics environment.
Keep your answer practical rather than theoretical. The interviewer is typically looking for a step-by-step approach, the kinds of SQL checks you would run, how you would treat NULLs and invalid values, and how you would document assumptions so the cleaned dataset can be trusted.