Kunal Khamar
Forward Deployed Software Engineer at Palantir Technologies
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolKunal Khamar is a Forward Deployed Software Engineer and Machine Learning Lead at Palantir with 11 years of experience building production-grade systems across finance and data infrastructure. He combines quantitative research experience from Citadel Securities with hands-on distributed-systems work—contributing to Apache Spark’s SQL and Structured Streaming core at Databricks—to deliver reliable recommender and streaming solutions. Comfortable operating at the intersection of ML and back-end engineering, he has a track record of diagnosing race conditions, improving streaming aggregation stability, and instrumenting cancelable streaming jobs. Based in New York, he blends research rigor from roles in quantitative trading with practical product delivery at large-scale platforms, and often surfaces non-obvious system-level fixes that improve operational robustness.
11 years of coding experience
1 year of employment as a software developer
Computer Science, Computer Science at The Hong Kong Polytechnic UniversityBachelor of Mathematics, Computer Science, Computational Math, Combinatorics & Optimization, Bachelor of Mathematics, Computer Science, Computational Math, Combinatorics & Optimization at University of WaterlooOntario Secondary School Diploma (OSSD), Ontario Secondary School Diploma (OSSD) at Middlefield Collegiate Institute
Hindi, Gujarati
Github Skills (8)
javas10
spark10
sql10
back-end-development10
java10
streaming10
scala10
testing9
Programming languages (2)
Github contributions (5)
apache/spark
Jan 2017 - Jul 2017
Apache Spark - A unified analytics engine for large-scale data processing
Role in this project:
Back-end Developer
Back-end DeveloperContributions:6 PRs, 89 comments in 5 months
Contributions summary:Kunal's contributions primarily involved enhancing the Apache Spark core functionality, particularly within the SQL and streaming components. Their work includes implementing features such as cloning `SessionState` with identical configurations, fixing a race condition, and adding a job group and description for streaming queries to properly cancel related jobs. The user also worked on improving the stability and usability of the streaming aggregation by managing the number of shuffle partitions.
analyticspythondata-processingsqlapache
kunalkhamar/spark
Feb 2017 - Apr 2017
Contributions:63 pushes, 7 branches in 2 months
apachebig-datasparkscalaapache-spark
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