Kuai Yu
Software Engineer at Google
San Jose, California, United States
Summary
🤩
Rockstar🎓
Top SchoolKuai Yu is a software engineer with nine years of experience building low-level and infrastructure software, currently at Google after prior engineering roles at LinkedIn and Brocade. He brings deep expertise in operating systems, C/C++, multi-threaded debugging, cross-compilation and virtualization tools like QEMU and Virsh, plus scripting in Perl and Python. Kuai has contributed to large-scale data integration work in Apache Gobblin—improving compaction for late data and adding security and metrics features—which complements his backend and data engineering skills. He also has working knowledge of graphics and rendering pipelines, OpenMP/OpenGL, and foundational machine learning, enabling cross-domain problem solving. Based in San Jose, he pairs rigorous debugging chops with a pragmatic approach to production systems and observability.
9 years of coding experience
8 years of employment as a software developer
Master, Computer Science, Master, Computer Science at University of Southern CaliforniaBachelor, Software Engineering, Bachelor, Software Engineering at Shanghai Jiao Tong University
Github Skills (14)
javas10
hadoop-mapreduce10
mapreduce10
hadoop10
data-pipeline10
data-pipelines10
java10
data-engineering10
apache9
metric9
kafka8
avro7
rest-api7
json6
Programming languages (2)
Github contributions (5)
apache/gobblin
Dec 2016 - Apr 2020
A distributed data integration framework that simplifies common aspects of big data integration such as data ingestion, replication, organization and lifecycle management for both streaming and batch data ecosystems.
Role in this project:
Back-end & Data Engineer
Back-end & Data EngineerContributions:1 release, 2 reviews, 144 commits in 3 years 5 months
Contributions summary:Kuai primarily worked on enhancing the Apache Gobblin framework, focusing on the data ingestion, replication, and lifecycle management aspects. Their contributions included implementing new features for compaction, particularly regarding the handling of late data and re-compacting files based on various thresholds. They also added support for password encryption and incorporated metrics for monitoring Eventhub data writers.
datadcosdata-streambig-data-integrationbatch-data
yukuai518/gobblin
Oct 2016 - Jun 2020
Universal data ingestion framework for Hadoop.
Contributions:295 pushes, 192 branches, 3 tags in 3 years 9 months
data-ingestionbig-datasparkhadoopingestion
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