basedosdados/queries-basedosdados
Oct 2024 – Feb 2025
3 Months active
Languages Used
PythonSQLYAML
Technical Skills
Data EngineeringData ModelingData ValidationETLPythonSQL
andre-ls
PROFILE
Andre-ls
Worked on the basedosdados/queries-basedosdados repository to enhance data models and pipelines for Brazilian tax collection and fire incident datasets. Focused on improving data quality and reliability by implementing partitioning, clustering, and schema validation in SQL, while developing Python scripts for data cleaning, extraction, and city-level mapping. Refactored models to align with updated directory schemas and consolidated validation tests to streamline data governance. Addressed environment consistency and staging references to ensure robust deployments. These efforts reduced manual data wrangling, increased dataset granularity, and enabled more accurate analytics, leveraging skills in data engineering, ETL, Python, and SQL throughout the development process.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
8Total
Bugs
2
Commits
8
Features
4
Lines of code
2,074
Activity Months3
Your Network
9 peopleWork History
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for basedosdados/queries-basedosdados: Delivered a major enhancement to the br_inpe_queimadas__microdados model and data pipeline by integrating monthly and yearly fire datasets, increasing data granularity and usability for city-level analyses. Implemented Python data extraction and city-mapping scripts, and expanded the dbt SQL model and repository schema with new columns and data types. The changes were tracked under commit c746c18cff41cf632b7a1b43178a3ac2880a2c43 and titled '[Dbt] Update br_inpe_queimadas_microdados (#922)'. This work reduces manual data wrangling, enables richer reporting, and sets the foundation for future feature expansions and improved decision support.
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for basedosdados/queries-basedosdados: Delivered a major enhancement to the br_inpe_queimadas__microdados model and data pipeline by integrating monthly and yearly fire datasets, increasing data granularity and usability for city-level analyses. Implemented Python data extraction and city-mapping scripts, and expanded the dbt SQL model and repository schema with new columns and data types. The changes were tracked under commit c746c18cff41cf632b7a1b43178a3ac2880a2c43 and titled '[Dbt] Update br_inpe_queimadas_microdados (#922)'. This work reduces manual data wrangling, enables richer reporting, and sets the foundation for future feature expansions and improved decision support.
February 2025
November 2024
5 Commits • 2 Features
Nov 1, 2024Concise monthly summary for 2024-11 focused on delivering business value and technical excellence for the basedosdados/queries-basedosdados repository.
November 2024
5 Commits • 2 Features
Nov 1, 2024Concise monthly summary for 2024-11 focused on delivering business value and technical excellence for the basedosdados/queries-basedosdados repository.
October 2024
2 Commits • 1 Features
Oct 1, 2024October 2024 monthly summary focusing on delivering data quality and reliability improvements in the br_rf_arrecadacao dataset within the queries-basedosdados repository. Key activities included data model enhancements with partitioning and clustering, development of Python data cleaning/formatting scripts for ITR and general revenue data, and updates to schema definitions to strengthen data validation. Also addressed environment consistency by correcting project naming and staging references, and added tests to validate changes. Overall, these efforts improve data quality, organization, and reliability, enabling faster, safer analytics and deployments.
2 Commits • 1 Features
Oct 1, 2024October 2024 monthly summary focusing on delivering data quality and reliability improvements in the br_rf_arrecadacao dataset within the queries-basedosdados repository. Key activities included data model enhancements with partitioning and clustering, development of Python data cleaning/formatting scripts for ITR and general revenue data, and updates to schema definitions to strengthen data validation. Also addressed environment consistency by correcting project naming and staging references, and added tests to validate changes. Overall, these efforts improve data quality, organization, and reliability, enabling faster, safer analytics and deployments.
October 2024
Activity
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Quality Metrics
Correctness92.6%
Maintainability90.0%
Architecture92.6%
Performance85.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonSQLYAML
Technical Skills
Data AnalysisData EngineeringData ModelingData ValidationDatabaseDatabase ManagementETLPythonSQL
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