Moonsoo Lee
Software Engineer at Databricks
San Francisco Bay Area United States
Summary
👤
Senior🎓
Top SchoolMoonsoo Lee is a software engineer and founder with 13+ years building distributed systems, cloud platforms, and enterprise AI products, currently advancing agentic and LLM-enabled features at Databricks. He created Apache Zeppelin—bootstrapping its codebase and a 300+ contributor community—which saw adoption by Apple, Twitter, Uber and integration into major cloud offerings. Moonsoo founded Staroid to deliver open-source projects as cloud services and helped commercialize Zeppelin through Zepl, gaining deep experience in enterprise product, support, and go-to-market dynamics. His contributions span backend database integrations, deployment and autoscaling for Ray, and serverless Spark in Databricks notebooks, reflecting a rare blend of hands-on engineering, DevOps, and product leadership. Based in the Bay Area with a master’s in distributed computing, he’s known for building remote engineering culture and sustainable open-source business models.
13 years of coding experience
15 years of employment as a software developer
Master's degree Distributed Computing, Master's degree Distributed Computing at Sogang University
Stackoverflow
156reputation
16kreached
7answers
1question
Github Skills (30)
autoscaling10
kubernetes10
database-api10
docker10
python10
databases10
jdbc10
dockers10
sql10
kubernetes-pods10
ray10
database10
java9
cloud-infrastructure9
javas9
Programming languages (11)
Github contributions (5)
apache/zeppelin
Jun 2016 - Mar 2021
Web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:1 review, 104 commits, 437 PRs in 4 years 10 months
Contributions summary:Moonsoo contributed significantly to the JDBC driver of the Apache Zeppelin project, focusing on functionality for interacting with databases. Their work included modifying existing methods to handle database connections, improving error handling, and adding functionalities for running SQL queries, including handling multiple statements, and displaying results. The commits demonstrate expertise in database interactions and the internal workings of the JDBC driver, with contributions spanning across several key methods and functionality for user interaction.
data-analyticsscalasqljavabig-data
ray-project/ray
Sep 2020 - Dec 2020
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Role in this project:DevOps Engineer & ML Engineer
DevOps Engineer & ML EngineerContributions:2 reviews, 9 commits, 9 PRs in 2 months
Contributions summary:Moonsoo primarily contributed to the project's infrastructure and deployment aspects, as evidenced by their work on Docker images and autoscaling configurations. They made modifications to the `build-docker.sh` script, configuring Python versions and image tags. Additionally, the user implemented a Staroid node provider for the autoscaler, integrating Ray with Staroid's Kubernetes-based infrastructure. These contributions demonstrate a focus on deployment, infrastructure management, and enabling Ray's functionality within a cloud environment.
aimachine-learningraydistributedparallel
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