Peng Chen
Senior Software Engineer at Google
Kirkland, Washington, United States
Summary
👤
Senior🎓
Top SchoolPeng Chen is a Senior Software Engineer and engineering lead based in Kirkland, WA, with nine years of experience building and optimizing large-scale distributed systems at AWS and Google. He has deep expertise in cloud messaging (SQS), leading features like server-side encryption and FIFO queues, and a knack for performance tuning to reduce cost and tail latency. Peng also contributes to data-quality tooling in open source—implementing KLL quantile sketches and constraint suggestion features for awslabs/deequ—bridging data science and backend engineering. He holds an MSc from the University of Calgary and a BS from Nanjing University of Aeronautics and Astronautics, blending strong academic signal processing roots with practical systems engineering. Colleagues describe him as someone who thrives on tough technical challenges and delivers pragmatic, high-impact solutions.
8 years of coding experience
8 years of employment as a software developer
Bachelor of Science (BS), Electrical and Electronics Engineering, 3.8, Bachelor of Science (BS), Electrical and Electronics Engineering, 3.8 at Nanjing University of Aeronautics and AstronauticsMaster of Science (MSc), Electrical and Electronics Engineering, 3.7, Master of Science (MSc), Electrical and Electronics Engineering, 3.7 at University of Calgary
Chinese, English
Github Skills (9)
apache-spark10
data-quality10
scala10
unit-test9
unit-testing9
algorithms8
data-structures8
data-structure8
machine-learning5
Programming languages (3)
Github contributions (5)
awslabs/deequ
Oct 2019 - Dec 2019
Deequ is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets.
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:5 commits, 4 PRs, 139 comments in 1 month
Contributions summary:Peng primarily contributed to the implementation of KLL (quantile) sketches within the Deequ library, which measures data quality. This involved creating and modifying code for the `QuantileNonSample` class, introducing features like merging and distance calculations for numerical and categorical data. Furthermore, the user enhanced the library by adding functionalities for constraint suggestions and KLL parameter configurations. This work directly supports the library's core function of data quality assessment.
data-qualityapacheunitsparkunit-tests
pengzai6666/deequ
Oct 2019 - Dec 2019
Deequ is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets.
Contributions:2 PRs, 40 pushes, 8 branches in 2 months
data-qualityapacheunitsparkunit-tests
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