dataforgoodfr/13_odis
Feb 2025 – Feb 2025
1 Month active
Languages Used
CSVPythonSQLYAML
Technical Skills
Configuration ManagementData AnalysisData EngineeringData ModelingData WarehousingDatabase Testing
SiangbaMM
PROFILE
Siangbamm
In February 2025, Michael Siangba developed a dbt-based geography data modeling and testing framework for the dataforgoodfr/13_odis repository. He established a scalable data layer centered on the com_dep_reg dimension, integrating seeds, automated unit-test macros, and baseline comparison files to streamline validation. Using SQL, Python, and dbt, Michael set up the core project structure, refined configuration management, and improved model scaffolding for repeatable releases. His work automated key aspects of database testing and reduced manual effort, enabling rapid feature delivery and more reliable geography analytics. The solution demonstrated depth in data modeling, ETL, and dependency management practices.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
9Total
Bugs
0
Commits
9
Features
1
Lines of code
36,929
Activity Months1
Your Network
18 peopleShared Repositories
18
Arthur HavlicekMember
kloudkraftsMember
apibracMember
antoningarMember
gustelleMember
HAMIDI AminMember
Henry D.Member
salma-hnanaMember
Jean-Baptiste-NMember
Work History
February 2025
9 Commits • 1 Features
Feb 1, 2025February 2025: Delivered the ODIS Geography Data Modeling and Testing Framework (dbt-based) in dataforgoodfr/13_odis. Established a scalable geography data layer with the com_dep_reg dimension, seeds and tests, plus an automated unit-test macro. Implemented core dbt setup, environment refinements, and comparison baselines to accelerate data quality checks. This work positions the project for rapid feature delivery and reliable geography-related analytics, with reduced manual testing effort.
9 Commits • 1 Features
Feb 1, 2025February 2025: Delivered the ODIS Geography Data Modeling and Testing Framework (dbt-based) in dataforgoodfr/13_odis. Established a scalable geography data layer with the com_dep_reg dimension, seeds and tests, plus an automated unit-test macro. Implemented core dbt setup, environment refinements, and comparison baselines to accelerate data quality checks. This work positions the project for rapid feature delivery and reliable geography-related analytics, with reduced manual testing effort.
February 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture87.8%
Performance82.2%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSVPythonSQLYAML
Technical Skills
Configuration ManagementData AnalysisData EngineeringData ModelingData WarehousingDatabase TestingDependency ManagementETLGitPostgreSQLSQLdbt
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