
Worked on CDCgov/NEDSS-DataReporting and related repositories to deliver five features and one bug fix over two months, focusing on public health informatics and microservices. Developed an HL7 Performance Testing ELR Generator to enhance performance testing, automated investigation creation from positive test results using workflow decision support, and modernized the local development environment by enabling Kafka KRaft and removing Zookeeper. Improved CI/CD reliability by updating GitHub Actions workflows, standardizing microservice naming, and refining deployment processes. Leveraged Python, YAML, and Docker to streamline integration testing, data generation, and deployment, resulting in faster, safer releases and improved alignment between development and production.
March 2026 performance summary for CDCgov/NEDSS-DataReporting. Delivered two strategic features and modernized the local development workflow to improve efficiency, reliability, and alignment with production. Key outcomes include automated investigation creation from positive test results via workflow decision support, and development environment modernization by removing Zookeeper and enabling Kafka KRaft for local development. Documentation and integration tests were updated to reflect the new local-dev workflow. Business impact: faster and more accurate disease investigations; reduced local dev setup friction; stronger alignment between development and production environments. Technologies and skills demonstrated: workflow decision support algorithms, Kafka KRaft adoption, development docs, and test adjustments, with clear cross-team collaboration.
March 2026 performance summary for CDCgov/NEDSS-DataReporting. Delivered two strategic features and modernized the local development workflow to improve efficiency, reliability, and alignment with production. Key outcomes include automated investigation creation from positive test results via workflow decision support, and development environment modernization by removing Zookeeper and enabling Kafka KRaft for local development. Documentation and integration tests were updated to reflect the new local-dev workflow. Business impact: faster and more accurate disease investigations; reduced local dev setup friction; stronger alignment between development and production environments. Technologies and skills demonstrated: workflow decision support algorithms, Kafka KRaft adoption, development docs, and test adjustments, with clear cross-team collaboration.
January 2026 – Performance-review-ready summary: Delivered key features and reliability improvements across three NEDSS repositories. HL7 Performance Testing ELR Generator added to CDCgov/NEDSS-DataReporting to boost performance testing capabilities. Modernization API deployment to Skylight AWS ECR and alignment of microservice workflows, with SonarQube scans temporarily disabled to unblock progress. Deployment workflow improvements for ingestion/processing/deduplication services, including ECR push fixes and temporary deduplication build halt. Fixed CI/CD issues such as service name typos and updated secrets to ensure accurate deployments. Together, these efforts increased testing coverage, reduced deployment failures, and standardized cross-repo naming, enabling faster, safer releases and more reliable data processing. Technologies demonstrated: HL7/ELR, AWS ECR/Skylight, GitHub Actions, containerized microservices, YAML workflows, and SonarQube workflow control.
January 2026 – Performance-review-ready summary: Delivered key features and reliability improvements across three NEDSS repositories. HL7 Performance Testing ELR Generator added to CDCgov/NEDSS-DataReporting to boost performance testing capabilities. Modernization API deployment to Skylight AWS ECR and alignment of microservice workflows, with SonarQube scans temporarily disabled to unblock progress. Deployment workflow improvements for ingestion/processing/deduplication services, including ECR push fixes and temporary deduplication build halt. Fixed CI/CD issues such as service name typos and updated secrets to ensure accurate deployments. Together, these efforts increased testing coverage, reduced deployment failures, and standardized cross-repo naming, enabling faster, safer releases and more reliable data processing. Technologies demonstrated: HL7/ELR, AWS ECR/Skylight, GitHub Actions, containerized microservices, YAML workflows, and SonarQube workflow control.

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