Yaliang Wang
Tech Lead Manager at WeRide.ai
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolYaliang Wang is a Tech Lead Manager in the San Francisco Bay Area with 11 years of experience building and scaling distributed data systems. He has deep backend expertise in high-performance SQL engines, contributing notable Hive and Parquet enhancements to widely used open-source projects Presto and Trino, including schema evolution for complex types and Parquet reader improvements. At Twitter he helped productionize Presto for petabyte-scale interactive analytics and led Thrift data support, and at WeRide.ai he progressed from software engineer to tech lead manager driving ML/AV-related infrastructure. He combines hands-on systems programming with team leadership, focusing on reliability, performance and secure data access across big-data stacks. A Northwestern MS graduate with roots in control engineering from Zhejiang University, he brings a quantitative mindset to distributed-query problems and a track record of impactful OSS contributions that improve interoperability in real-world deployments.
11 years of coding experience
7 years of employment as a software developer
The University of British ColumbiaMaster of Science (M.S.) Computer Science, Master of Science (M.S.) Computer Science at Northwestern UniversityBachelor of Engineering (BEng) Automation (Control), Bachelor of Engineering (BEng) Automation (Control) at Zhejiang University
Chinese, English
Github Skills (17)
big-data10
java10
javas10
hive10
sql10
parquet10
presto10
data-set9
sql-query9
data-model9
databases9
user-data9
database9
data-structures8
distributed-database8
Programming languages (3)
Github contributions (5)
prestodb/presto
Oct 2017 - Feb 2019
The official home of the Presto distributed SQL query engine for big data
Role in this project:
Back-end Developer
Back-end DeveloperContributions:12 commits, 21 PRs, 94 comments in 1 year 4 months
Contributions summary:Yaliang primarily contributed to the Presto query engine, focusing on enhancements to the Hive connector. Their work includes adding schema evolution support for structural types, specifically for arrays, maps, and row types. They also added support for case-insensitive column lookup in Parquet readers and made changes to improve the prefetching of the Parquet footer. Additionally, the user implemented impersonation tests for custom ORC/RCFILE writers and addressed issues related to Kerberos authentication.
distributed-sqlquerybigdataquery-enginesql
trinodb/trino
Feb 2019 - Feb 2019
Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)
Role in this project:Backend Developer
Backend DeveloperContributions:1 PR, 1 comment in 9 days
Contributions summary:Yaliang primarily contributed to the Hive connector within the Trino project, focusing on schema evolution support for structural types such as arrays, maps, and structs. They implemented coercion logic and made modifications to the HiveCoercionRecordCursor. Further contributions included adding struct and complex columns to test tables within the Hive connector and removing an option from the CLI related to Kerberos authentication. Additionally, the user added the support of column names in parquet readers.
prestodbdbmsindexingjdbcbigdata
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