Hoki Tam is a software engineer based in New York with over a decade of experience across search, NLP, onboarding, and observability at companies including Google, Twitter, and Datadog. He blends deep technical craftsmanship—contributions to Datadog’s dd-trace-java agent and data-stream checkpointing for RabbitMQ/Kafka—with product-minded work driving user onboarding and cross-functional roadmaps. At Google he led small teams improving internal natural language representations and query understanding used across Search and Assistant, demonstrating both technical depth and leadership. Comfortable across full-stack and backend systems, he has a track record of shipping reliability and automation that reduces manual work for organizations. His background from MIT and early systems work at VMware and Boston Scientific underscores a long-standing focus on robust engineering and scalable tooling. Notably, his open-source tracing contributions reflect a practical focus on observability in distributed Java systems.
4 years of coding experience
11 years of employment as a software developer
S.B., Computer Science and Engineering, S.B., Computer Science and Engineering at Massachusetts Institute of Technology
Contributions:59 reviews, 70 commits, 35 PRs in 5 months
Contributions summary:Hoki contributed to the Datadog APM client for Java, making several changes related to the pathway context and data streams. Their work included updating and refactoring code in `DefaultPathwayContext.java`, renaming methods in `CoreTracer.java`, and implementing methods for `ExtractedPathwayContext`. They also integrated data streams checkpointing within the RabbitMQ and Kafka client instrumentation. The changes reflect a focus on distributed tracing and data stream monitoring within the Java agent.
Contributions:17 reviews, 58 commits, 20 PRs in 8 months
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