Jason Teoh
Software Engineer at Databricks
Seattle, Washington, United States
Summary
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Rockstar🎓
Top SchoolJason Teoh is a software engineer with 10 years of experience building large-scale data infrastructure and streaming systems, currently developing at Databricks in Seattle. He blends industry and research expertise—holding a PhD from UCLA focused on debugging and performance of big data systems—with production work at Twitter and LinkedIn on Beam/Flink pipelines, OLAP platforms, and real-time anomaly detection. Jason has contributed to notable open-source projects like Apache Pinot by improving ThirdEye anomaly detection logic and alerting, and he has a track record designing multi-region, multi-cloud ingestion and scalable Airflow pipelines for enterprise customers. Comfortable moving between low-level performance diagnostics and high-level product roadmaps, he also brings hands-on experience integrating analytics backends and automating operational tooling. An often-overlooked strength is his history of teaching and mentoring in university courses, which sharpens his ability to explain complex systems and build developer-focused tooling.
10 years of coding experience
5 years of employment as a software developer
Bachelor of Arts (B.A.), Computer Science, Bachelor of Arts (B.A.), Computer Science at University of California, BerkeleyUniversity of California, Los Angeles
Stackoverflow
1reputation
0reached
2answers
0questions
Github Skills (10)
javas10
apache-pinot10
anomaly-detection10
back-end-development10
java10
testing9
rdd6
spark-streaming6
scala6
dstream6
Programming languages (5)
Github contributions (5)
apache/pinot
Jun 2016 - Jun 2016
Apache Pinot - A realtime distributed OLAP datastore
Role in this project:
Back-end Developer
Back-end DeveloperContributions:14 commits, 36 PRs, 33 pushes in 27 days
Contributions summary:Jason primarily focused on enhancing the functionality of the ThirdEye anomaly detection system. They modified core Java files within the thirdeye-pinot module to adjust anomaly message formats, refine data analysis logic, and optimize email report generation. These changes included implementing new dependency management and refining time window calculations for graphs, ultimately improving the accuracy and presentation of anomaly alerts. They also added test cases for PQLUtils.
apache-pinotolapjava
maligulzar/bigdebug
Feb 2018 - Feb 2020
Contributions:146 commits, 1 push, 1 branch in 2 years
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