Bart Brinke is a data-driven software engineer and Master’s student in Data Science & Business Analytics with 17 years of professional experience, currently completing a thesis internship at KLM in Rotterdam. He blends practical engineering—evidenced by backend contributions to open-source Ruby tools like the well-regarded Brakeman security scanner and a request log analyzer—with applied data science skills used to build and deploy NLP models for a B2B SaaS product. His background spans customer-facing roles and product analytics, giving him a knack for translating business problems into data-informed, production-ready solutions. Known for pragmatic refactors and lightweight visualizations (ASCII graphing in log analysis), he favors readable, maintainable code and measurable impact.
18 years of coding experience
1 year of employment as a software developer
vwo, vwo at CSG Augustinus
Master's degree Data Science and Business Analytics, Master's degree Data Science and Business Analytics at University of Amsterdam
Create reports based on your log files. Supports Rails, Apache, MySQL, Delayed::Job, and other formats.
Role in this project:
Back-end Developer
Contributions:303 commits, 5 comments in 6 years 3 months
Contributions summary:Bart primarily focused on enhancing the request log analyzer's functionality by introducing features like timestamping and an ASCII-graph visualization. They modified existing code to include timestamps in the logs and implemented an ASCII-based graph for request time analysis. The code modifications involved changes to the log parsing and summarization logic.
A static analysis security vulnerability scanner for Ruby on Rails applications
Role in this project:
Back-end Developer
Contributions:11 commits in 3 months
Contributions summary:Bart primarily contributed to improving the security scanner, Brakeman, for Ruby on Rails applications. Their work involved refactoring code, specifically in the `check_link_to` class, by breaking down functions and removing code duplication. They implemented a fall-through mechanism in the argument checking process and optimized the codebase by switching from `map` to `each`. Additionally, they refactored the report generation, creating a renderer object for HTML output.
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