Matthew Wong is a software engineer with a decade of experience building scalable, cost-efficient distributed systems and cloud storage integrations. A UCLA CS graduate and current Master's student, he helped design and deploy Kafka Tiered Storage compaction at Confluent—migrating petabytes to object storage and saving $4M/year across AWS, GCP, and Azure. His open-source contributions to Apache Kafka focused on performance, reliability, and tooling refinements, evidencing deep backend and systems expertise. Now at Anduril, he brings practical experience in cloud-native data platforms, low-level C integrations from past internships, and a knack for translating complex systems work into operational savings. Outside engineering he’s an avid gamer and competitive swimmer, interests that reflect his mix of strategic focus and disciplined practice.
10 years of coding experience
6 years of employment as a software developer
High School Diploma 4.70 / 4.0, High School Diploma 4.70 / 4.0 at University High School
Contributions:38 reviews, 6 commits, 7 PRs in 1 year 11 months
Contributions summary:Matthew primarily focused on improving the Apache Kafka project's performance and reliability through bug fixes and feature enhancements. Their contributions involved increasing consumer timeouts, enabling test functions, and streamlining tombstone and transaction marker removal within the Kafka log cleaner. The user's work also extended to updating documentation and the DumpLogSegments tool, reflecting changes in the record batch schema.
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