Leo Folsom developed a monthly scheduling system for monitors and data workflows in the datafold/demo repository, shifting from daily or frequent intervals to a standardized cadence on the first day of each month. He implemented these changes by updating YAML-based production job definitions and the data generation pipeline, leveraging skills in workflow automation, CI/CD, and DevOps. This approach preserved data freshness and alerting while optimizing resource usage and reducing operational overhead across platforms like BigQuery, Databricks, and Dremio. Leo’s work improved maintainability and deployment confidence by consolidating scheduling logic within configuration files, enabling more predictable and efficient operations.