Parth Upadhyay
Software Engineer at Watershed
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolParth Upadhyay is a software engineer with 14 years of experience building large-scale, reliable data and backend systems across the US and overseas, currently working at Watershed in New York. He has deep expertise in data platforms and ETL at petabyte scale, having driven cost-saving, replication, validation, and access-control initiatives at Samsara that saved millions annually and improved data confidence. Comfortable across the full stack, Parth has led cross-functional teams to deliver complex reporting and analytics features using Spark/Databricks, AWS, Dagster, and modern frontend/back-end stacks. He’s an active open-source contributor to high-profile projects like Apache Spark, improving Protobuf handling for enums and unsigned integers — a detail that reflects his attention to correctness in distributed data processing. His background includes building consumer-facing backend services at LINE and Coursera, and an early career blending SRE and teaching roles, which shapes a pragmatic, mentor-oriented approach to engineering.
14 years of coding experience
9 years of employment as a software developer
B.S Computer Science, B.S Computer Science at The University of Texas at Austin
English, Japanese
Github Skills (9)
javas10
big-data10
spark10
protobuffer10
protobuf10
java10
scala10
sql4
jdbc4
Programming languages (5)
Github contributions (5)
apache/spark
Mar 2023 - Jan 2024
Apache Spark - A unified analytics engine for large-scale data processing
Role in this project:
Back-end Developer
Back-end DeveloperContributions:52 reviews, 8 PRs, 73 comments in 10 months
Contributions summary:Parth primarily contributed to the `spark-protobuf` connector within the Apache Spark project. Their work focused on enhancing the serialization and deserialization of Protobuf messages, specifically handling enum types and unsigned integer types. Key contributions include adding the functionality to deserialize enums as integers and the ability to upcast unsigned integers to larger types. Additionally, the user addressed a bug related to repeated enum fields and implemented well-known wrapper types, improving the library's flexibility and correctness.
analyticspythondata-processingsqlapache
justaparth/spark
Mar 2023 - Jan 2024
Apache Spark - A unified analytics engine for large-scale data processing
Contributions:1 review, 4 PRs, 64 pushes in 10 months
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