Worked on foundational maintainability improvements to the snowflakedb/ArcticTraining data pipelines, focusing on code organization and data loading using Python. Centralized the DataLoader creation process by introducing a base DataFactory and subclass-specific collate functions, which reduced code duplication and improved maintainability. Refactored evaluation logging by encapsulating the log iteration condition into a dedicated function, enhancing both readability and correctness. Addressed persistent worker considerations to ensure scalable and reliable data loading in production environments. These changes laid the groundwork for faster onboarding and safer future enhancements, delivering a more robust and adaptable codebase for ongoing development and training workflows.