Chris refactored Airflow-based data pipelines in the GoogleCloudPlatform/ml-auto-solutions repository, focusing on improving the readability and maintainability of DAGs. By replacing ad-hoc task chaining with Airflow’s chain utility across multiple DAG files, Chris standardized task sequencing and clarified execution flow. This approach reduced code complexity, making pull request reviews more straightforward and accelerating onboarding for new data engineers. The work, implemented in Python and leveraging Airflow’s orchestration features, reinforced pipeline reliability while maintaining a clear rollback path. Chris’s targeted changes aligned with workflow improvement initiatives, enhancing code quality and traceability without introducing risk or altering existing pipeline functionality.