Andrey Gubichev
Staff Software Engineer at Databricks
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolAndrey Gubichev is a Staff Software Engineer in the San Francisco Bay Area with 13 years of experience building and optimizing large-scale query engines. He holds a PhD in Computer Science from TU Munich and has driven query optimizer work at Databricks after multi-year engineering roles at Google and Celonis. Andrey is an active back-end contributor to Apache Spark, where his fixes to Spark SQL (scalar subqueries, correlated predicates, window functions and ORDER BY/LIMIT handling) improved correctness and performance for large analytic workloads. His background spans research and industry—Oracle Labs, Max Planck, and academic internships—giving him a deep blend of theoretical rigor and production-grade systems engineering. Colleagues know him for surfacing subtle correctness bugs in query planning that others miss and for translating those insights into robust optimizer changes.
13 years of coding experience
8 years of employment as a software developer
Master of Science (MSc) Computer Science, Master of Science (MSc) Computer Science at Saint Petersburg State UniversityDoctor of Philosophy (Ph.D.) Computer Science, Doctor of Philosophy (Ph.D.) Computer Science at Technical University of Munich
Github Skills (10)
javas10
big-data10
sql-query10
query-optimization10
spark10
window-functions10
sql10
subquery10
java10
scala10
Programming languages (3)
Github contributions (5)
apache/spark
May 2023 - Jan 2025
Apache Spark - A unified analytics engine for large-scale data processing
Role in this project:
Back-end Developer
Back-end DeveloperContributions:96 reviews, 17 PRs, 108 comments in 1 year 8 months
Contributions summary:Andrey's contributions primarily involved enhancing the Apache Spark SQL module. They focused on addressing correctness bugs within scalar subqueries, specifically concerning nullability and correlated references in join predicates, and implemented support for window functions. The user also worked on handling `LIMIT` and `ORDER BY` clauses in correlated subqueries, improving the handling of count bugs, and managing predicates over correlated subqueries, contributing to a more robust and functional SQL engine. These changes included code modifications to improve query optimization and execution.
analyticspythondata-processingsqlapache
agubichev/spark
May 2023 - Nov 2024
Apache Spark - A unified analytics engine for large-scale data processing
Contributions:113 pushes, 17 branches in 1 year 6 months
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