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HamsterGulloso

PROFILE

Hamstergulloso

Daniel Vasconcelos enhanced the prefeitura-rio/pipelines_rj_sms repository by building and refining data extraction pipelines focused on scalability and maintainability. He introduced sliced query execution and per-slice processing in Metabase data extraction, enabling support for databases without unique IDs and robust date filtering. Using Python, BigQuery, and Prefect, Daniel improved code readability, standardized parameters, and removed redundant code to reduce technical debt. He also fixed critical issues in BigQuery table mapping and schedule flow parameter generation, ensuring accurate data loading and reliable scheduling. His work deepened the pipeline’s reliability, broadened data source compatibility, and improved long-term maintainability for data engineering workflows.

Overall Statistics

Feature vs Bugs

60%Features

Repository Contributions

8Total
Bugs
2
Commits
8
Features
3
Lines of code
1,075
Activity Months3

Work History

October 2025

1 Commits

Oct 1, 2025

October 2025: Stabilized schedule flow parameter generation in prefeitura-rio/pipelines_rj_sms by fixing table data access to include IDs, ensuring parameters are populated with accurate information. No new features released this month; the primary work focused on a critical bug fix that enhances data integrity and reliability of scheduling workflows. This change reduces downstream errors and improves trust in automated schedules across the pipeline.

September 2025

2 Commits • 1 Features

Sep 1, 2025

September 2025: Delivered critical reliability and scalability improvements to the Metabase Data Lake ingestion for prefeitura-rio/pipelines_rj_sms. Implemented fixes to ensure data is loaded into the correct BigQuery tables and datasets and expanded extraction coverage with new table IDs and slice configurations, enabling broader analytics and faster onboarding of new data sources.

August 2025

5 Commits • 2 Features

Aug 1, 2025

August 2025 monthly summary focusing on delivering scalable data extraction improvements for prefeitura-rio/pipelines_rj_sms and enhancing maintainability of the data extraction pipeline. The work enables sliced queries, per-slice processing, and support for databases without a unique ID, along with robust date filtering, while simplifying task flows and reducing technical debt.

Activity

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Quality Metrics

Correctness82.6%
Maintainability85.0%
Architecture78.8%
Performance72.6%
AI Usage22.6%

Skills & Technologies

Programming Languages

Python

Technical Skills

API IntegrationBigQueryData EngineeringData PipelinesData WarehousingETLMetabasePrefectPython

Repositories Contributed To

1 repo

Overview of all repositories you've contributed to across your timeline

prefeitura-rio/pipelines_rj_sms

Aug 2025 Oct 2025
3 Months active

Languages Used

Python

Technical Skills

API IntegrationData EngineeringData PipelinesData WarehousingETLMetabase

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