dataforgoodfr/13_pollution_eau
Feb 2025 – Feb 2025
1 Month active
Languages Used
MarkdownPythonSQL
Technical Skills
Data EngineeringDatabase ManagementDocumentationETLScripting
Hubert SIMOES
PROFILE
Hubert Simoes
During February 2025, Hubert Simoes developed an automated data update capability for the EDC dataset within the dataforgoodfr/13_pollution_eau repository. He refactored the existing build-database script using Python and SQL to support a repeatable refresh workflow, introducing a drop-tables option and data-modification checks to ensure data quality during updates. This engineering work streamlined the data refresh process, reducing manual intervention and improving the accuracy and timeliness of downstream analytics. Hubert also updated the project documentation in Markdown to reflect these changes, enhancing onboarding and maintenance clarity. His contributions focused on data engineering, ETL, and database management practices.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
237
Activity Months1
Your Network
12 peopleShared Repositories
12
Work History
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for dataforgoodfr/13_pollution_eau: Delivered automated data update capability for the EDC dataset, including a drop-tables option and data-modification checks; refactored the build-database script to support a refresh workflow and updated related documentation. This work reduces manual refresh effort, improves data accuracy, and accelerates data-to-insight timelines for downstream analytics and reporting.
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for dataforgoodfr/13_pollution_eau: Delivered automated data update capability for the EDC dataset, including a drop-tables option and data-modification checks; refactored the build-database script to support a refresh workflow and updated related documentation. This work reduces manual refresh effort, improves data accuracy, and accelerates data-to-insight timelines for downstream analytics and reporting.
February 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
MarkdownPythonSQL
Technical Skills
Data EngineeringDatabase ManagementDocumentationETLScripting
Repositories Contributed To
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