Wei Song
Principal Staff Engineer at LinkedIn
Fremont, California, United States
Summary
🤩
Rockstar🎓
Top SchoolWei Song is a Principal Staff Engineer with over a decade of experience designing and delivering high‑availability distributed systems and cloud migrations, currently leading LinkedIn’s Data and AI infrastructure migration to Microsoft Azure. He combines hands‑on implementation skills with architectural vision across streaming, storage, networking and service frameworks, and is an Apache Samza committer who introduced the Samza Table API and RocksDB support. Wei’s background spans carrier‑grade video and VoIP solutions at Cisco, SaaS infrastructure at Aerohive, and large scale e‑commerce platform architecture at VIP.com, reflecting deep expertise in performance, scalability and operational reliability. He contributes to notable open‑source projects like Brooklin, improving stream reliability and Avro→JSON tooling, showing a practical focus on robustness and recoverability. Based in Fremont, CA, he blends systems‑level networking knowledge (TCP/IP, SNMP, VoIP/SIP) with modern cloud patterns (Kafka, Redis, Spark) to bridge legacy telecom systems and cloud‑native architectures. Colleagues rely on him for solving thorny production problems and shaping long‑term platform strategy.
11 years of coding experience
16 years of employment as a software developer
Bachelor Electrical Engineering, Bachelor Electrical Engineering at Beijing University of Posts and TelecommunicationsMaster's degree Elektrotechnik, Master's degree Elektrotechnik at RWTH Aachen University
Chinese, German, English
Github Skills (10)
javas10
kafka10
debug10
data-streaming10
java10
data-engineering9
testing9
avro9
distributed-systems8
change-data-capture8
Programming languages (1)
Github contributions (5)
linkedin/brooklin
Sep 2016 - Aug 2017
An extensible distributed system for reliable nearline data streaming at scale
Role in this project:
Backend Developer
Backend DeveloperContributions:21 commits in 11 months
Contributions summary:Wei primarily contributed to the backend functionality of the `brooklin` project, focusing on improving data streaming reliability and performance. Their work included fixing bugs related to event producer pool size and thread termination monitoring, which are essential for system stability. Furthermore, the user implemented features for Avro to JSON conversion and added the capability to skip bad messages.
complex-event-processingstreamingdistributed-systemsextensiblescalability
weisong44/samza
Oct 2017 - Jun 2019
Mirror of Apache Samza
Contributions:55 pushes, 47 branches in 1 year 8 months
javasamzaapache
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