Daniel Cuesta worked on improving machine learning data storage configuration in the pass-culture/data-gcp repository, focusing on storage path hygiene and policy alignment. He initially introduced a temporary ML storage bucket configuration to better organize artifacts and reduce misconfiguration risks, then refined the approach by reverting to a more streamlined setup using MLFLOW_BUCKET_NAME. His work involved careful management of cloud storage paths and configuration management, ensuring that temporary and permanent ML artifacts were clearly separated. Using Python and leveraging skills in cloud configuration and data engineering, Daniel delivered targeted, maintainable changes that addressed both immediate needs and long-term repository organization.