Context

Financial datasets often contain missing values, inconsistent date formats, duplicate transactions, and invalid amounts. In analytics and reporting, poor data wrangling leads directly to incorrect revenue, expense, and cash flow numbers.

Question

Explain how you would perform data wrangling on a messy financial dataset using SQL in PostgreSQL. Your answer should cover:

1. How to identify and handle missing or invalid values
2. How to standardize dates, categories, and numeric fields
3. How to detect duplicates and suspicious records
4. How to prepare the cleaned data for downstream reporting or aggregation

Scope Guidance

The interviewer is looking for a practical SQL-oriented explanation, not a full ETL architecture. Focus on common cleaning patterns, the order of operations, and how you would make the dataset reliable enough for financial analysis.