Your question is Pandas Data Cleaning Scenario. 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).
Data engineers are often expected to move comfortably between SQL and Python when a dataset needs quick profiling, cleanup, or reshaping before loading into a PostgreSQL-based pipeline.
Describe a situation where you used Python's Pandas library for data manipulation. You should explain the dataset you worked with, the data quality or transformation issues you found, the specific Pandas operations you used, and the output you produced. Focus on practical manipulation tasks such as filtering rows, handling missing values, standardizing columns, deduplicating records, and creating summary aggregations.
Keep your answer concrete and implementation-focused. The interviewer is looking for a simple end-to-end example that shows you understand when Pandas is useful alongside SQL, what functions you used, and how you validated the result before passing the data into a downstream process such as a Coforge data pipeline or PostgreSQL load.