MTES-MCT/qualicharge
Feb 2025 – Oct 2025
6 Months active
Languages Used
MarkdownPythonSQLYAMLpythonyaml
Technical Skills
API integrationData migrationMetabasePython scriptingAPI DesignCI/CD
philippe thomy
PROFILE
Philippe Thomy
Philippe Thomy developed and maintained data quality and migration tools for the MTES-MCT/qualicharge repository, focusing on backend systems that support robust data governance and analytics. He engineered Python-based migration scripts and Prefect workflows to automate Metabase question transfers and enforce data quality expectations, leveraging SQL and Great Expectations for validation and integrity checks. His work included optimizing deployment configurations, implementing dynamic and static data validation suites, and refining API documentation to clarify data model constraints. By addressing both feature development and bug fixes, Philippe ensured reliable, maintainable pipelines that improved data traceability, reduced manual effort, and supported scalable production deployments.
Overall Statistics
Feature vs Bugs
89%Features
Repository Contributions
23Total
Bugs
2
Commits
23
Features
16
Lines of code
4,496
Activity Months6
Your Network
85 people
Work History
October 2025
6 Commits • 3 Features
Oct 1, 2025Month: 2025-10 – MTES-MCT/qualicharge
6 Commits • 3 Features
Oct 1, 2025Month: 2025-10 – MTES-MCT/qualicharge
October 2025
September 2025
5 Commits • 3 Features
Sep 1, 2025Month: 2025-09. Overview for MTES-MCT/qualicharge: delivered key features to strengthen data quality governance and reporting reliability. Key features delivered include dynamic data quality checks for ENEU and session data, expanded quality expectations for statuses, and clarified energy rule documentation. Major bug fixed: NE10 quality expectation bug fix to reduce false positives. Overall impact: improved data integrity, timeliness, and governance of energy data, enabling better risk management and data-driven decisions. Technologies/skills demonstrated: data quality frameworks, dynamic checks, regex corrections, test-driven development, Prefect workflows, API/schema documentation, and configuration management.
September 2025
5 Commits • 3 Features
Sep 1, 2025Month: 2025-09. Overview for MTES-MCT/qualicharge: delivered key features to strengthen data quality governance and reporting reliability. Key features delivered include dynamic data quality checks for ENEU and session data, expanded quality expectations for statuses, and clarified energy rule documentation. Major bug fixed: NE10 quality expectation bug fix to reduce false positives. Overall impact: improved data integrity, timeliness, and governance of energy data, enabling better risk management and data-driven decisions. Technologies/skills demonstrated: data quality frameworks, dynamic checks, regex corrections, test-driven development, Prefect workflows, API/schema documentation, and configuration management.
August 2025
1 Commits • 1 Features
Aug 1, 20252025-08 Monthly Summary for MTES-MCT/qualicharge: Data Quality Validation Suite implemented for the Prefect system, introducing static data quality expectations and robust data integrity checks across power, location, charging point, and identifier formats. SQL query fixes and updated tests completed to reinforce reliability and reduce data-related issues. This work enhances data governance, reduces downstream defects, and supports safer deployments and more accurate analytics.
1 Commits • 1 Features
Aug 1, 20252025-08 Monthly Summary for MTES-MCT/qualicharge: Data Quality Validation Suite implemented for the Prefect system, introducing static data quality expectations and robust data integrity checks across power, location, charging point, and identifier formats. SQL query fixes and updated tests completed to reinforce reliability and reduce data-related issues. This work enhances data governance, reduces downstream defects, and supports safer deployments and more accurate analytics.
August 2025
May 2025
2 Commits • 1 Features
May 1, 2025May 2025 - MTES-MCT/qualicharge: Delivered reliability improvements for Prefect indicators and optimized historicization. Key changes include fixing variance calculation to ensure non-negative indicator values, refactoring indicator retrieval into a reusable task, and reducing historical data retention across deployment configurations to lower storage and compute costs. Updated CHANGELOG.md and prefect.yaml to reflect changes, enabling smoother rollouts and maintainability.
May 2025
2 Commits • 1 Features
May 1, 2025May 2025 - MTES-MCT/qualicharge: Delivered reliability improvements for Prefect indicators and optimized historicization. Key changes include fixing variance calculation to ensure non-negative indicator values, refactoring indicator retrieval into a reusable task, and reducing historical data retention across deployment configurations to lower storage and compute costs. Updated CHANGELOG.md and prefect.yaml to reflect changes, enabling smoother rollouts and maintainability.
April 2025
8 Commits • 7 Features
Apr 1, 2025April 2025 monthly summary for MTES-MCT/qualicharge: Delivered new operational indicators and strengthened deployment workflows to improve data quality, governance, and production readiness. Focused on expanding monitoring capabilities, robust Prefect configurations, and enhanced documentation to support scalable data submissions and governance.
8 Commits • 7 Features
Apr 1, 2025April 2025 monthly summary for MTES-MCT/qualicharge: Delivered new operational indicators and strengthened deployment workflows to improve data quality, governance, and production readiness. Focused on expanding monitoring capabilities, robust Prefect configurations, and enhanced documentation to support scalable data submissions and governance.
April 2025
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for MTES-MCT/qualicharge highlighting key accomplishments in features and bugs, overall impact, and technologies demonstrated.
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for MTES-MCT/qualicharge highlighting key accomplishments in features and bugs, overall impact, and technologies demonstrated.
Activity
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Quality Metrics
Correctness88.2%
Maintainability85.2%
Architecture85.6%
Performance76.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
MarkdownPythonSQLYAMLpythonyaml
Technical Skills
API DesignAPI IntegrationAPI integrationBackend DevelopmentCI/CDConfiguration ManagementData EngineeringData QualityData ValidationData migrationDatabase Query OptimizationDevOpsDocumentationGreat ExpectationsMetabase
Repositories Contributed To
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