Rachel Wells restructured and standardized data assets for the datakind/student-success-tool repository, focusing on improving data management and workflow efficiency. She consolidated synthetic data and the data generator script into a new sample-platform directory, reducing cross-repository dependencies and simplifying onboarding for future contributors. Using Python and Git, Rachel implemented consistent naming conventions and reorganized CSV files to align with the new structure, which supports reproducible synthetic data generation in CI pipelines. Her work laid the foundation for automated data provisioning and streamlined test data generation, demonstrating a methodical approach to maintainability and future scalability within the project’s data infrastructure.