NMDSdevopsServiceAdm/DataEngineering
Mar 2026 – Mar 2026
1 Month active
Languages Used
DockerfilePython
Technical Skills
DockerPolarsPySparkPythonPython developmentPython programming
Mary Akowe
PROFILE
Mary Akowe
Worked on the NMDSdevopsServiceAdm/DataEngineering repository to deliver faster and more reliable data processing by integrating Polars-based utilities and re-enabling the PySpark pipeline for end-to-end workflows. Enhanced deployment and testing readiness through Docker and Fargate scaffolding, while improving code quality with linting and expanded unit tests. Simplified the data model by cleaning up schemas and removing unnecessary columns, which reduced maintenance risk and streamlined downstream analytics. Addressed data quality by refining outlier detection and cleaning logic, particularly for repeated values. Utilized Python, Docker, and Polars to implement robust data engineering solutions focused on maintainability, testing, and analytics performance.
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
50 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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