Worked on the IDM4s-2025/df-manipulation642 repository to lay the foundation for a scalable data pipeline by introducing initial data ingestion assets, specifically train.csv and test.csv files. Focused on repository hygiene, the developer updated the .gitignore to prevent unnecessary data exposure and subsequently removed mistakenly committed data files, reducing the repository’s footprint and ensuring clean version control. The work emphasized reproducibility and streamlined integration for downstream processes. Leveraged skills in data engineering, file management, and data deletion, with a focus on handling CSV files. The approach demonstrated careful attention to both technical setup and best practices in data pipeline preparation.