Carl Brasic
Lead Software Engineer at DRW
Chicago, Illinois, United States
Summary
🤩
Rockstar🎓
Top SchoolCarl Brasic is a Lead Software Engineer based in Chicago with 12 years of experience building high-performance backend systems. He specializes in performance optimization and refactoring, demonstrated by impactful contributions to widely used Ruby projects like Shopify's tapioca (speeding up RBI generation) and RuboCop (improving ResultCache and runtime efficiency). At DRW he combines hands-on coding with technical leadership to deliver robust, production-ready solutions. A UIUC computer science graduate, he brings a practical, test-driven approach to tackling encoding, caching, and string-processing edge cases that often lurk behind slow developer tooling.
12 years of coding experience
University of Illinois Urbana-Champaign
Github Skills (15)
ruby10
caching10
sorbet10
performance-optimization10
package-development10
testing9
static-code-analysis8
code-formatting8
formatters7
rubygems7
formater7
sql-formatter7
rubocop7
formatter7
linter7
Programming languages (12)
Github contributions (5)
Shopify/tapioca
Dec 2021 - Jan 2022
The swiss army knife of RBI generation
Role in this project:
Back-end Developer
Back-end DeveloperContributions:1 review, 5 commits, 2 PRs in 11 days
Contributions summary:Carl focused on optimizing the `tapioca` gem, specifically improving the performance of the `RequiresCompiler`. Their contributions included refactoring code to reduce redundant operations, addressing encoding issues, and refactoring to avoid gemspec requirements. They also added tests to handle Ruby source files with encodings other than UTF-8. The user's work directly impacted the speed and robustness of the `tapioca require` command.
swissruby-gemsgem-rbisarmysorbet
rubocop/rubocop
Apr 2018 - Apr 2018
A Ruby static code analyzer and formatter, based on the community Ruby style guide.
Role in this project:Performance Engineer
Performance EngineerContributions:5 commits, 1 PR, 1 comment in 6 days
Contributions summary:Carl focused on optimizing the performance of RuboCop's code analysis and formatting. Their commits primarily involved memoization and refactoring of the `ResultCache` class. The user implemented techniques such as memoizing `Dir.pwd`, using shorter config strings, leveraging incremental digesters, and employing faster string concatenation methods, significantly improving runtime performance.
linterstyle-guidecode-formattercode-analyzerruby
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