Younghee Kwon
Staff Software Engineer at Google
Mountain View, California, United States
Summary
👤
Senior🎓
Top SchoolYounghee Kwon is a Staff Software Engineer at Google with over 15 years of experience building large-scale data processing and machine learning infrastructure. Based in Mountain View, she has driven columnar analysis platforms and contributed to distributed SQL and ML frameworks since joining Google in 2008. Her open-source work includes meaningful contributions to Apache Beam and Airflow—adding TFRecord IO, performance optimizations for the DirectRunner, and Cloud ML Engine integration—reflecting a blend of backend engineering and data/QA rigor. She holds BS/MS/PhD degrees from KAIST and brings a research-rooted perspective from prior roles at KAIST and the Max Planck Institute. Known for improving developer experience and runtime efficiency, she often focuses on pragmatic, testable changes that scale in production. Colleagues rely on her for deep systems thinking that bridges academic rigor and production-grade engineering.
9 years of coding experience
1 year of employment as a software developer
Highschool, Highschool at Korea Science Academy of KAISTBS, MS, PhD, Computer Science, BS, MS, PhD, Computer Science at KAIST
Korean, English
Stackoverflow
51reputation
493reached
2answers
0questions
Github Skills (29)
apache-airflow10
dataflow-programming10
dataflow10
python10
data-engineering10
testing10
workflow-engine10
gcp10
mlops10
cloud-storage10
orchestration10
apache-beam10
machine-learning9
automations8
apidoc8
Programming languages (4)
Github contributions (5)
apache/airflow
May 2017 - Nov 2019
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Role in this project:
ML Engineer & Data Engineer
ML Engineer & Data EngineerContributions:8 PRs, 25 comments in 2 years 6 months
Contributions summary:Younghee contributed significantly to the integration of Google Cloud ML Engine within the Apache Airflow platform. Their work included adding utilities for model evaluation using Cloud ML, including components for prediction and dataflow based summarization, and validation. This involved developing operators, hooks, and utils related to MLEngine and Cloud Storage. Furthermore, they made changes to documentation, demonstrating a focus on improving the user experience and maintainability of the integration features.
monitorpythonschedulerapacheprogrammatically
apache/beam
Dec 2016 - Feb 2017
Apache Beam is a unified programming model for Batch and Streaming data processing.
Role in this project:Back-end Developer & QA Engineer
Back-end Developer & QA EngineerContributions:9 commits, 12 PRs, 63 comments in 1 month
Contributions summary:Younghee made several contributions related to improving the Python SDK of Apache Beam. They fixed example usages in Python pipeline code, updated comments regarding PTransform methods, and added tests for AvroIO, skipping them when Snappy is not installed. They also created TFRecordIO, providing source and sink capabilities for TFRecords within the Beam framework and refactored tests to clean temporary directories. Furthermore, they improved the performance of the DirectRunner by optimizing the BoundedReadEvaluator and added a MemoryReporter for heap profile tracking.
golangpythonstreaming-databeambatch
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