Worked on the great-expectations/great_expectations repository to enhance data integrity during asset ingestion, specifically addressing the handling of boolean values in CSV asset parsing. Focused on the dynamic pandas asset reader, the work involved delivering a targeted bug fix that prioritized boolean-like types during type sorting, ensuring that boolean values were preserved rather than coerced to strings. This change reduced downstream type errors in validation pipelines and improved the reliability of data validation and reporting. The solution was developed using Python and pandas, with an emphasis on data validation, unit testing, and collaborative code review practices to ensure robust implementation.