
Daniel Higgins developed core data processing features for the codeforboston/boston-liquor-license-tracker repository, focusing on automated extraction and normalization of licensing data from PDF documents. He engineered a PDF scraper that converts unstructured text into structured JSON, enabling downstream analytics and improving data accessibility. Leveraging Python, Daniel rebuilt the project’s scaffolding to streamline onboarding and standardized environment setup. He also introduced a modular PDF processing pipeline with a plugin architecture, allowing targeted post-processing without altering core logic. By implementing address normalization using the Boston SAM dataset, Daniel enhanced data quality and established a foundation for scalable, maintainable document processing workflows.
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.

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