NMDSdevopsServiceAdm/DataEngineering
Mar 2026 – Mar 2026
1 Month active
Languages Used
DockerfilePython
Technical Skills
DockerPolarsPySparkPythonPython developmentPython programming
Mary Akowe
PROFILE
Mary Akowe
Mary Akowe enhanced the NMDSdevopsServiceAdm/DataEngineering repository by delivering Polars-based data processing improvements, enabling faster and cleaner data transformations. She re-enabled and validated the PySpark pipeline for end-to-end processing, while simplifying the data model through schema cleanup. Using Python and Docker, Mary introduced new cleaning utilities, improved outlier detection logic, and added Docker/Fargate scaffolding to streamline deployment and testing workflows. Her work included robust unit testing, code linting, and documentation updates, resulting in more reliable analytics and reduced maintenance risk. The depth of her contributions strengthened data quality, deployment readiness, and the overall maintainability of the engineering workflow.
Overall Statistics
Feature vs Bugs
83%Features
Repository Contributions
32Total
Bugs
2
Commits
32
Features
10
Lines of code
2,490
Activity Months1
Your Network
39 peopleWork History
March 2026
32 Commits • 10 Features
Mar 1, 2026NMDSdevopsServiceAdm/DataEngineering – March 2026 monthly summary: Delivered Polars-based data processing improvements, enabling faster transformations through polars_utils and new cleaning utilities. Restored end-to-end pipeline by enabling the PySpark job. Hardened deployment and testing readiness with Docker/Fargate scaffolding and test scaffolding. Performed data model simplification via data/schema cleanup (removing the latest column). Enhanced data quality and reliability with compute_outlier_cutoff_and_clean improvements to handle repeated values more robustly. Overall, contributed to faster, more reliable data processing, improved analytics capabilities, and reduced maintenance risk.
32 Commits • 10 Features
Mar 1, 2026NMDSdevopsServiceAdm/DataEngineering – March 2026 monthly summary: Delivered Polars-based data processing improvements, enabling faster transformations through polars_utils and new cleaning utilities. Restored end-to-end pipeline by enabling the PySpark job. Hardened deployment and testing readiness with Docker/Fargate scaffolding and test scaffolding. Performed data model simplification via data/schema cleanup (removing the latest column). Enhanced data quality and reliability with compute_outlier_cutoff_and_clean improvements to handle repeated values more robustly. Overall, contributed to faster, more reliable data processing, improved analytics capabilities, and reduced maintenance risk.
March 2026
Activity
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Quality Metrics
Correctness85.6%
Maintainability84.4%
Architecture82.6%
Performance81.8%
AI Usage23.8%
Skills & Technologies
Programming Languages
DockerfilePython
Technical Skills
DockerPolarsPySparkPythonPython developmentPython programmingPython scriptingclean code practicescode lintingdata analysisdata cleaningdata engineeringdata manipulationdata processingsoftware development
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