dataforgoodfr/13_odis
May 2025 – Jul 2025
3 Months active
Languages Used
JinjaSQLYAMLPython
Technical Skills
Data ModelingETLSQLdbtData EngineeringDatabase Management
Victorien-M
PROFILE
Victorien-m
Victorien Maillet developed and enhanced data models for the dataforgoodfr/13_odis repository, focusing on education and demographic analytics over a three-month period. He designed multi-layered data pipelines using dbt, SQL, and Python, integrating geographic references to enable location-aware reporting and policy analysis. His work unified and enriched education and population data models across bronze, silver, and gold layers, improving data quality, lineage, and governance. Victorien also introduced new data sources and maintained pipeline stability through dependency management and configuration updates. The depth of his contributions ensured reliable, reproducible analytics and supported robust, geo-enabled insights for downstream users.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
18Total
Bugs
0
Commits
18
Features
5
Lines of code
1,124
Activity Months3
Your Network
18 peopleShared Repositories
18
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HAMIDI AminMember
Henry D.Member
salma-hnanaMember
Jean-Baptiste-NMember
Work History
July 2025
2 Commits • 2 Features
Jul 1, 2025July 2025 monthly summary for dataforgoodfr/13_odis: Focused on strengthening data completeness and pipeline stability by delivering a new data source and applying maintenance updates.
2 Commits • 2 Features
Jul 1, 2025July 2025 monthly summary for dataforgoodfr/13_odis: Focused on strengthening data completeness and pipeline stability by delivering a new data source and applying maintenance updates.
July 2025
June 2025
13 Commits • 2 Features
Jun 1, 2025June 2025 performance summary for dataforgoodfr/13_odis: Delivered two major data modeling initiatives that significantly improve cross-layer analytics, data quality, and governance for education and demographic metrics. The work enables geo-enabled insights and more reliable reporting for education outcomes and socio-economic indicators. Key achievements include: - Enriched and unified Education Moyenne Eleve data model across bronze/silver/gold layers with geo references (department/region), and improved data quality for the gold layer, enabling consistent, geography-based analysis. This involved a series of commits to evolve the education_moyenne_eleve data pipeline and related gold views. - Matured population data models by introducing bronze/silver for population_categorie_socio_pro and launching population_nb_menages with a silver view and final silver/gold references, including lineage fixes to support accurate analyses and dashboards. - Strengthened data lineage and governance across both features, ensuring traceability from source to gold and facilitating reproducible analyses for downstream reporting. - Demonstrated end-to-end technical capabilities in data modeling, cross-layer unification, geo-enrichment, and data quality improvements, delivering tangible business value for education metrics and demographic insights. Technologies/skills demonstrated: - Data modeling (bronze/silver/gold layers, data enrichment, model generalization) - Geo enrichment and geographical references - Data lineage and governance practices - ETL/ELT processes and commit-driven development - Cross-functional collaboration and maintainability across the repository
June 2025
13 Commits • 2 Features
Jun 1, 2025June 2025 performance summary for dataforgoodfr/13_odis: Delivered two major data modeling initiatives that significantly improve cross-layer analytics, data quality, and governance for education and demographic metrics. The work enables geo-enabled insights and more reliable reporting for education outcomes and socio-economic indicators. Key achievements include: - Enriched and unified Education Moyenne Eleve data model across bronze/silver/gold layers with geo references (department/region), and improved data quality for the gold layer, enabling consistent, geography-based analysis. This involved a series of commits to evolve the education_moyenne_eleve data pipeline and related gold views. - Matured population data models by introducing bronze/silver for population_categorie_socio_pro and launching population_nb_menages with a silver view and final silver/gold references, including lineage fixes to support accurate analyses and dashboards. - Strengthened data lineage and governance across both features, ensuring traceability from source to gold and facilitating reproducible analyses for downstream reporting. - Demonstrated end-to-end technical capabilities in data modeling, cross-layer unification, geo-enrichment, and data quality improvements, delivering tangible business value for education metrics and demographic insights. Technologies/skills demonstrated: - Data modeling (bronze/silver/gold layers, data enrichment, model generalization) - Geo enrichment and geographical references - Data lineage and governance practices - ETL/ELT processes and commit-driven development - Cross-functional collaboration and maintainability across the repository
May 2025
3 Commits • 1 Features
May 1, 20252025-05 monthly summary for dataforgoodfr/13_odis. Key feature delivered: education data model with geographic integration, including bronze sources for population socio-professional categories and a silver layer to aggregate education data across communes, departments, and regions. No major bugs fixed this month. Overall impact: enables richer, location-aware educational analytics in ODIS, supporting data-driven policy decisions and cross-regional insights. Technologies/skills demonstrated: data modeling (bronze/silver layers), geographic data integration, ETL/warehousing, and code traceability via commits.
3 Commits • 1 Features
May 1, 20252025-05 monthly summary for dataforgoodfr/13_odis. Key feature delivered: education data model with geographic integration, including bronze sources for population socio-professional categories and a silver layer to aggregate education data across communes, departments, and regions. No major bugs fixed this month. Overall impact: enables richer, location-aware educational analytics in ODIS, supporting data-driven policy decisions and cross-regional insights. Technologies/skills demonstrated: data modeling (bronze/silver layers), geographic data integration, ETL/warehousing, and code traceability via commits.
May 2025
Activity
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Quality Metrics
Correctness84.4%
Maintainability85.6%
Architecture83.4%
Performance77.8%
AI Usage20.0%
Skills & Technologies
Programming Languages
JinjaPythonSQLYAML
Technical Skills
Data EngineeringData ModelingDatabase ManagementDependency ManagementETLPython PackagingSQLSQL Developmentdbt
Repositories Contributed To
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