Problem
Explain your approach
Describe how you ensure a SQL analysis is accurate when the underlying financial data is incomplete, duplicated, delayed, or inconsistent. Focus on how you inspect data quality, validate joins, handle missing values, and avoid producing misleading metrics.
What to Cover
- How you profile raw data before analysis
- How you validate join logic and row-count changes
- How you handle NULLs, duplicates, and conflicting records
- How you communicate assumptions and reconcile final outputs
Why It Matters
In portfolio and recovery reporting, small data issues can materially change balances, liquidation rates, or payment trends. The interviewer is looking for a methodical SQL workflow, not just a generic statement about “cleaning the data.”
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