NMDSdevopsServiceAdm/DataEngineering
Feb 2026 – Mar 2026
2 Months active
Languages Used
JSONMarkdownPythonYAML
Technical Skills
API integrationAWSAWS GlueCI/CDCircleCICode Quality
Mitch Edmunds (MadeTech)
PROFILE
Mitch Edmunds (madetech)
Mitchell Edmunds developed and enhanced the NMDSdevopsServiceAdm/DataEngineering repository, focusing on robust data pipeline architecture and deployment reliability. Over two months, he standardized naming conventions, improved schema alignment, and introduced time-series imputation and rolling sum computations to support advanced forecasting. His work included refactoring the pipeline into a class-based structure, expanding test coverage with Pytest, and optimizing CI/CD workflows using CircleCI and Terraform. Leveraging Python and Polars, Mitchell implemented reusable utilities for label coalescing and percentage share calculations, while modernizing dependency management. The result was a maintainable, test-driven codebase with improved data governance and streamlined deployment processes.
Overall Statistics
Feature vs Bugs
70%Features
Repository Contributions
251Total
Bugs
27
Commits
251
Features
63
Lines of code
98,195
Activity Months2
Your Network
39 peopleWork History
March 2026
131 Commits • 32 Features
Mar 1, 2026March 2026 (Month: 2026-03) – Summary for NMDSdevopsServiceAdm/DataEngineering Key features delivered - Rolling Sum Architecture and Test Refactor: implemented rolling sum with tests, wrapped the 6-month rolling sum as a function and reimplemented as an expression; refactored tests and data fixtures for configurability and clarity; updated time column to cqc_location_import_date and added period configurability. - Polars Expressions integration for rolling_sum with percentage_share: updated percentage_share to work with polars expressions and enable reuse with rolling_sum; removed _expr suffix for consistency. - Data model clarity: renamed unix_col to import_date_col for clarity. - CI/CD and packaging updates: added Pipfile.lock, migrated CI to uv, updated cache location, removed sudo, and installed packages at system level for reliability. - Testing and data utilities: added coalesce_labels function; introduced coalesced and source label column steps in pipeline; moved input_lf to fixture for testing isolation; documentation and changelog updates; ivy2 cache added to speed up builds. Major bugs fixed - Percentage share application correctness: ensured percentage_share is applied over the correct groups (location and date). - Intermediate sum column restored: revert to creating an intermediate sum column to avoid windowing confusion in the Polars backend. - API compatibility risk: removed unused GHA_Actor and GHA_Event to acknowledge potential breaking changes. - Spark config issues: fixed bind address in Spark config and later reverted as needed to stabilize CI. - Percentage share zero-sum handling: define and reuse total in zero-sum scenarios; added tests for edge cases. - Has elements handling: added explicit null case handling and parametrized tests; removed unused has_elements later as part of cleanup. Overall impact and accomplishments - Delivered a robust, test-driven data pipeline capable of flexible rolling computations, improved data model clarity, and stronger data governance. - Modernized CI/CD and build caching, speeding feedback loops and increasing deployment reliability. - Built a maintainable codebase with reusable utilities (coalesce_labels), centralized labeling logic, and a class-based pipeline structure enabling easier future enhancements. - Expanded test coverage, improved data integrity, and enhanced documentation/readability for ongoing maintenance. Technologies/skills demonstrated - Python, Polars expressions, and PyTest with extensive parametrization - Data pipeline design including rolling window computations and label coalescing - CI/CD optimization (Pipfile.lock, uv-based CI, ivy2 cache, system-level installs) - Data modeling enhancements (import_date_col), dataclass-based filtering, and class-based architecture
131 Commits • 32 Features
Mar 1, 2026March 2026 (Month: 2026-03) – Summary for NMDSdevopsServiceAdm/DataEngineering Key features delivered - Rolling Sum Architecture and Test Refactor: implemented rolling sum with tests, wrapped the 6-month rolling sum as a function and reimplemented as an expression; refactored tests and data fixtures for configurability and clarity; updated time column to cqc_location_import_date and added period configurability. - Polars Expressions integration for rolling_sum with percentage_share: updated percentage_share to work with polars expressions and enable reuse with rolling_sum; removed _expr suffix for consistency. - Data model clarity: renamed unix_col to import_date_col for clarity. - CI/CD and packaging updates: added Pipfile.lock, migrated CI to uv, updated cache location, removed sudo, and installed packages at system level for reliability. - Testing and data utilities: added coalesce_labels function; introduced coalesced and source label column steps in pipeline; moved input_lf to fixture for testing isolation; documentation and changelog updates; ivy2 cache added to speed up builds. Major bugs fixed - Percentage share application correctness: ensured percentage_share is applied over the correct groups (location and date). - Intermediate sum column restored: revert to creating an intermediate sum column to avoid windowing confusion in the Polars backend. - API compatibility risk: removed unused GHA_Actor and GHA_Event to acknowledge potential breaking changes. - Spark config issues: fixed bind address in Spark config and later reverted as needed to stabilize CI. - Percentage share zero-sum handling: define and reuse total in zero-sum scenarios; added tests for edge cases. - Has elements handling: added explicit null case handling and parametrized tests; removed unused has_elements later as part of cleanup. Overall impact and accomplishments - Delivered a robust, test-driven data pipeline capable of flexible rolling computations, improved data model clarity, and stronger data governance. - Modernized CI/CD and build caching, speeding feedback loops and increasing deployment reliability. - Built a maintainable codebase with reusable utilities (coalesce_labels), centralized labeling logic, and a class-based pipeline structure enabling easier future enhancements. - Expanded test coverage, improved data integrity, and enhanced documentation/readability for ongoing maintenance. Technologies/skills demonstrated - Python, Polars expressions, and PyTest with extensive parametrization - Data pipeline design including rolling window computations and label coalescing - CI/CD optimization (Pipfile.lock, uv-based CI, ivy2 cache, system-level installs) - Data modeling enhancements (import_date_col), dataclass-based filtering, and class-based architecture
March 2026
February 2026
120 Commits • 31 Features
Feb 1, 2026February 2026 monthly summary for NMDSdevopsServiceAdm/DataEngineering focusing on delivering business value through naming standardization, increased test stability, and deployment reliability across the data engineering stack. Demonstrated strong end-to-end ownership from data governance (naming and schema alignment) to pipeline reliability (tests, CI, Terraform) and advanced data handling (time-series imputation and interpolation).
February 2026
120 Commits • 31 Features
Feb 1, 2026February 2026 monthly summary for NMDSdevopsServiceAdm/DataEngineering focusing on delivering business value through naming standardization, increased test stability, and deployment reliability across the data engineering stack. Demonstrated strong end-to-end ownership from data governance (naming and schema alignment) to pipeline reliability (tests, CI, Terraform) and advanced data handling (time-series imputation and interpolation).
Activity
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Quality Metrics
Correctness96.2%
Maintainability93.4%
Architecture93.0%
Performance93.0%
AI Usage20.6%
Skills & Technologies
Programming Languages
JSONMarkdownPythonYAML
Technical Skills
API integrationAWSAWS GlueCI/CDCircleCICode QualityCode RefactoringConfiguration ManagementContinuous IntegrationData EngineeringDependency ManagementDevOpsDocumentationETLGitHub Actions
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