
Worked on the cal-itp/data-infra repository to deliver three new features focused on data modeling and workflow automation. Developed and validated new TIDES data models for vehicle locations and trips, supporting public GTFS-RT feeds and per-agency exports with high data quality and historical accuracy. Enhanced the CI/CD pipeline by integrating Graphviz into GitHub Actions, enabling automated visual model reporting during builds. Refactored dbt models to implement SCD Type 2 joins and introduced persistent source_record_id fields, ensuring stable identity across data versions. Leveraged SQL, YAML, and dbt to improve data integrity, validation, and maintainability within a scalable engineering framework.
May 2026 performance summary: Graphviz-enabled CI for model reports, new TIDES data models for vehicles and trips, and robust data integrity improvements to support reliable public GTFS-RT feeds and per-agency exports. Focused on delivering business value through faster feedback, historical accuracy, and scalable data exposure, backed by dbt modeling, CI/CD enhancements, and data quality controls.
May 2026 performance summary: Graphviz-enabled CI for model reports, new TIDES data models for vehicles and trips, and robust data integrity improvements to support reliable public GTFS-RT feeds and per-agency exports. Focused on delivering business value through faster feedback, historical accuracy, and scalable data exposure, backed by dbt modeling, CI/CD enhancements, and data quality controls.

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