Worked on the aws/aws-sdk-pandas repository to enhance the robustness of Parquet data ingestion in dataset mode. Addressed a bug where dtype inference could fail if the first partition was empty by implementing a pre-merge filtering step that excludes empty tables before merging. This approach ensures that downstream data processing pipelines in Python remain reliable and prevents silent dtype changes or pipeline errors. The solution included comprehensive regression tests to validate correct handling of empty partitions, maintaining compatibility with existing APIs. Leveraged skills in AWS SDK, data engineering, and unit testing to improve the reliability of Parquet read workflows.