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flowchart LR
A[HRSA Data Warehouse] -->|HTTPS Request| B(Ingest Script)
B -->|Raw .xlsx| C{Clean & Transform}
C -->|Pandas| D[Oregon Subset .csv]
D -->|Quarto| E[Static HTML Site]
E -->|Deploy| F[GitHub Pages]
style B fill:#e3f2fd
style C fill:#e3f2fd
style D fill:#e3f2fd
style E fill:#e3f2fd
classDef runner fill:#e3f2fd,stroke:#1976d2,stroke-width:2px
class B,C,D,E runner
Technical Architecture
Under the hood of the Oregon FQHC Landscape
Architecture
Python
CI/CD
Overview
This project represents a strategic shift from manual, GUI-based reporting (Tableau) to Code-First Analytics Engineering.
The goal was to build a self-healing, automated data product that tracks Health Center Service Delivery Sites in Oregon without requiring manual intervention.
Advanced Analytics & QA
Data Enrichment: Successfully ingested 2024 UDS Grantee Data via direct FOIA endpoint, implementing a robust pandas cleaning layer to handle mixed numeric/text columns in government Excel files.
Relational Joining: Engineered a deterministic join between HRSA geospatial data and UDS patient demographics using BHCMISID keys, achieving a 97% match rate (318/328 sites).
Automated Quality Assurance: Integrated pytest into the CI/CD pipeline. The system now enforces data integrity checks (e.g., Uninsured < Total Patients, Lat/Lon within Oregon bounds) before every deployment, preventing “silent failures” in production.
The Pipeline
The system is architected as a serverless ETL pipeline running on GitHub Actions.