
Worked on the codeforboston/boston-liquor-license-tracker repository, building a robust data ingestion system for Boston licensing data. Developed a PDF scraper that extracts text from licensing board documents and transforms it into structured JSON, enabling automated analytics. Rebuilt the project’s scaffolding to standardize environment setup and streamline onboarding. Introduced a modular PDF processing pipeline with a plugin architecture, allowing targeted post-processing of text anomalies without altering core logic. Added an address normalization component leveraging the Boston SAM dataset to improve data quality. Focused on Python programming, data extraction, and web scraping, emphasizing maintainability, extensibility, and reliable data processing workflows throughout.
February 2026 monthly summary for codeforboston/boston-liquor-license-tracker focused on data quality improvements and extensible processing architecture. Delivered two major features that set up safer, scalable data workflows and established a foundation for future bug fixes without risking core logic.
February 2026 monthly summary for codeforboston/boston-liquor-license-tracker focused on data quality improvements and extensible processing architecture. Delivered two major features that set up safer, scalable data workflows and established a foundation for future bug fixes without risking core logic.
January 2026 monthly summary focusing on key accomplishments for codeforboston/boston-liquor-license-tracker. Delivered a robust data ingestion component and improved project onboarding and maintainability, enabling automated licensing data extraction and downstream analytics.
January 2026 monthly summary focusing on key accomplishments for codeforboston/boston-liquor-license-tracker. Delivered a robust data ingestion component and improved project onboarding and maintainability, enabling automated licensing data extraction and downstream analytics.

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