Patrick Strawderman is a senior software engineer with 17 years of experience building scalable backend systems and platforms, currently focused on machine learning and platform modernization at Netflix in San Francisco. He has a strong Java-centered engineering pedigree, contributing meaningful fixes and refactors to high-profile open-source projects like GraphQL Java and the Spring Framework that improved performance, safety, and maintainability. At Netflix he progressed through experimentation and platform roles, shaping infrastructure used for large-scale experiments and metric collection. Earlier work at Amazon and Zope honed his expertise in high-throughput services, REST APIs, and cross-platform apps. Patrick favors pragmatic modernizations—replacing legacy collections, tightening type-safety, and optimizing memory and stream handling—to reduce operational overhead. He pairs deep hands-on coding with a sustained open-source footprint that surfaces non-obvious gains in robustness and efficiency.
17 years of coding experience
16 years of employment as a software developer
Bachelor of Science (B.S.) Computer Science, Bachelor of Science (B.S.) Computer Science at University of Mary Washington
Contributions:103 reviews, 41 commits, 320 PRs in 1 year 8 months
Contributions summary:Patrick primarily contributed to the DGS framework, a GraphQL implementation for Java with Spring Boot. Their commits focused on improving code quality and fixing bugs. They addressed issues in the metrics and client modules, refactoring code for better performance and maintainability, and introducing code style enhancements. They also worked on dependency alignment and the transition to more modern Java idioms.
Contributions:4 reviews, 14 commits, 21 PRs in 6 years 9 months
Contributions summary:Patrick primarily focused on improving type safety and eliminating raw types and unchecked casts within the `spectator-api` project. They refactored code related to the `Functions` class, including methods for invoking methods and handling gauge functionality. Additionally, they implemented optimizations for `ArrayTagSet`, including special-case merging and size calculations for more efficient tag management. These changes improved the overall robustness and maintainability of the project's core metrics collection functionality.
client-librarymeasurementcollectingmetricsjava
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Patrick Strawderman - Senior Software Engineer, Machine Learning Platform