prefeitura-rio/pipelines_rj_smtr
Aug 2025 – Oct 2025
3 Months active
Languages Used
PythonYAMLbashyaml
Technical Skills
BigQueryCI/CDCloudData EngineeringPythonSQL
Boris Araujo
PROFILE
Boris Araujo
Over a three-month period, Boris Marinho engineered automated metadata synchronization and data quality enhancements for the prefeitura-rio/pipelines_rj_smtr repository. He developed a CI/CD workflow in Python that parses dbt models and updates BigQuery table and column descriptions, addressing encoding and length constraints to improve metadata governance. Boris also enabled production-targeted dbt checks and implemented label propagation from dbt models to BigQuery tables using YAML-driven configuration, strengthening data lineage and quality controls. Additionally, he resolved critical import errors in metadata update scripts, ensuring reliable scheduled executions. His work demonstrated depth in Python, BigQuery, dbt, and CI/CD pipeline automation.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
6Total
Bugs
1
Commits
6
Features
3
Lines of code
222
Activity Months3
Your Network
5 peopleWork History
October 2025
1 Commits
Oct 1, 2025October 2025 monthly summary for prefeitura-rio/pipelines_rj_smtr focused on reliability and maintainability of the data pipeline. Addressed a critical import-related issue in a metadata update script to prevent runtime failures and ensure smooth, scheduled executions in production.
1 Commits
Oct 1, 2025October 2025 monthly summary for prefeitura-rio/pipelines_rj_smtr focused on reliability and maintainability of the data pipeline. Addressed a critical import-related issue in a metadata update script to prevent runtime failures and ensure smooth, scheduled executions in production.
October 2025
September 2025
2 Commits • 2 Features
Sep 1, 2025Month: 2025-09 | Concise monthly summary focused on business value and technical achievements for prefeitura-rio/pipelines_rj_smtr. Key features delivered: - Enable dbt checks against production environment: Updated the dbt-checks GitHub Action to target the production environment by setting the dbt target to 'prod' and ensuring the production profile is used for data quality checks against the production data warehouse. (Commit: 38b05fc42b43935d7f3acedd15eb55aa35e70f79) - Propagate labels from dbt models to parent BigQuery tables: Implemented label propagation by reading an allowlist of tags from a YAML file and applying them as labels to BigQuery tables; added new Python functions and updated the main execution flow. (Commit: cb0539183ae45dea73457c325fc4b7a6601ab642) Major bugs fixed: - No critical defects reported in this period; focus remained on feature delivery and pipeline reliability. Overall impact and accomplishments: - Strengthened production data quality checks and governance, reducing risk of data quality issues in production and improving traceability of data lineage through consistent labeling. - Accelerated time-to-detection for production data issues and improved maintainability of the data quality pipeline through code and workflow enhancements. Technologies/skills demonstrated: - dbt and dbt-checks, GitHub Actions, Python, BigQuery, YAML-driven configuration, data governance labeling, CI/CD automation.
September 2025
2 Commits • 2 Features
Sep 1, 2025Month: 2025-09 | Concise monthly summary focused on business value and technical achievements for prefeitura-rio/pipelines_rj_smtr. Key features delivered: - Enable dbt checks against production environment: Updated the dbt-checks GitHub Action to target the production environment by setting the dbt target to 'prod' and ensuring the production profile is used for data quality checks against the production data warehouse. (Commit: 38b05fc42b43935d7f3acedd15eb55aa35e70f79) - Propagate labels from dbt models to parent BigQuery tables: Implemented label propagation by reading an allowlist of tags from a YAML file and applying them as labels to BigQuery tables; added new Python functions and updated the main execution flow. (Commit: cb0539183ae45dea73457c325fc4b7a6601ab642) Major bugs fixed: - No critical defects reported in this period; focus remained on feature delivery and pipeline reliability. Overall impact and accomplishments: - Strengthened production data quality checks and governance, reducing risk of data quality issues in production and improving traceability of data lineage through consistent labeling. - Accelerated time-to-detection for production data issues and improved maintainability of the data quality pipeline through code and workflow enhancements. Technologies/skills demonstrated: - dbt and dbt-checks, GitHub Actions, Python, BigQuery, YAML-driven configuration, data governance labeling, CI/CD automation.
August 2025
3 Commits • 1 Features
Aug 1, 2025Monthly summary for 2025-08 focusing on feature delivery in prefeitura-rio/pipelines_rj_smtr. Implemented automated synchronization of dbt metadata to BigQuery descriptions via CI/CD, ensuring that table and column documentation stays in lockstep with dbt definitions. The workflow parses dbt models to generate a manifest and applies descriptions to BigQuery, addressing encoding and description-length constraints to improve governance and metadata accuracy.
3 Commits • 1 Features
Aug 1, 2025Monthly summary for 2025-08 focusing on feature delivery in prefeitura-rio/pipelines_rj_smtr. Implemented automated synchronization of dbt metadata to BigQuery descriptions via CI/CD, ensuring that table and column documentation stays in lockstep with dbt definitions. The workflow parses dbt models to generate a manifest and applies descriptions to BigQuery, addressing encoding and description-length constraints to improve governance and metadata accuracy.
August 2025
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Quality Metrics
Correctness93.4%
Maintainability93.4%
Architecture93.4%
Performance90.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonYAMLbashyaml
Technical Skills
BigQueryCI/CDCloudData EngineeringDevOpsMetadata ManagementPythonSQLdbt
Repositories Contributed To
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