Shun-ping Chiu
Senior Software Engineering Manager at Pinterest
Sunnyvale, California, United States
Summary
👤
Senior🎓
Top SchoolShun-ping Chiu is a Senior Software Engineering Manager based in Sunnyvale with eight years of experience building large-scale streaming and indexing systems for companies like Pinterest and LinkedIn. He leads teams to deliver real-time and batch pipelines using Kafka, Flink, and Spark to power features such as sequence-based engagement signals, vector/graph embeddings for ANN search, and production-grade streaming inference. Previously at LinkedIn he helped standardize member-profile signals and contributed as a core developer to Brooklin, LinkedIn’s well-known Kafka mirroring system, implementing smart partition assignment and robust streaming logic. Combining hands-on backend competence with people leadership, he bridges ML-driven feature development and scalable data infrastructure. An electrical engineering graduate with an MS from CMU, he has practical experience launching experiments that directly inform ads, search, and personalization.
8 years of coding experience
10 years of employment as a software developer
BS, Electrical Engineering, BS, Electrical Engineering at National Taiwan UniversityPhd drop out, Phd drop out at Carnegie Mellon University
Chinese, English
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (7)
javas10
kafka10
data-streaming10
java10
data-engineer9
change-data-capture9
distributed-systems9
Programming languages (2)
Github contributions (5)
linkedin/brooklin
Sep 2018 - Oct 2019
An extensible distributed system for reliable nearline data streaming at scale
Role in this project:
Back-end Developer
Back-end DeveloperContributions:43 commits, 35 PRs, 27 pushes in 1 year
Contributions summary:Shun-ping primarily contributed to the `brooklin` project by implementing features related to data streaming and task management. Their work involved adding configurable parameters to the `StickyMulticastStrategy`, managing the partition assignment for the Kafka Mirror Maker, and improving the data streaming logic related to Kafka. They also addressed issues with the handling of data stream states. Furthermore, the user made improvements to javadocs.
complex-event-processingstreamingdistributed-systemsextensiblescalability
Jyouping/brooklin
Jul 2019 - Oct 2019
An extensible distributed system for reliable nearline data streaming at scale
Contributions:123 pushes, 30 branches in 2 months
stream-processingstreaming-datacomplex-event-processingdistributed-systemstreaming
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