Navina Ramesh
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top SchoolNavina Ramesh is a seasoned software engineer with 13 years of experience building feed personalization and distributed data systems, notably contributing backend improvements to the high-profile Apache Pinot realtime OLAP datastore. Based in Mountain View, she drove core feed and stream-processing work at LinkedIn—improving pagination, integrating lightweight Feed APIs, and evolving Apache Samza for elastic, library-style deployments across diverse environments. Comfortable across Java, Scala, C, Python and more, she combines systems-level rigor from early large-scale operations at Yahoo with research-informed work from her MS in Computer Engineering. A pragmatic open-source contributor, she has improved streaming consumer interfaces and added production-grade metrics and non-Kafka realtime ingestion to Pinot, reflecting a focus on reliability and observability. Colleagues know her for quietly tackling core platform complexity so product teams can ship faster.
13 years of coding experience
12 years of employment as a software developer
Kendriya Vidyalaya IITMB.Tech., Information Technology, B.Tech., Information Technology at College of Engineering, Guindy, Anna UniversityMS, Computer Engineering, MS, Computer Engineering at University of Florida
Stackoverflow
160reputation
2kreached
5answers
1question
Github Skills (12)
javas10
apache-pinot10
stream-processing10
java10
metric9
kafka9
amazon-kinesis8
kin8
partition6
google-cloud-pubsub6
apache-kafka6
apache-samza6
Programming languages (4)
Github contributions (5)
apache/pinot
Jan 2022 - Jan 2023
Apache Pinot - A realtime distributed OLAP datastore
Role in this project:
Back-end Developer
Back-end DeveloperContributions:277 reviews, 24 commits, 42 PRs in 1 year
Contributions summary:Navina primarily focused on enhancing and maintaining the Apache Pinot codebase, particularly in its back-end components. Their contributions include removing outdated broker metric definitions, enhancing the Pinot streaming consumer interface, and allowing realtime data providers to accept non-Kafka producers. They also addressed bugs related to KinesisDataProducer and added a new broker metric for total query processing time. The user was involved in several code changes that involved core components of the Pinot platform.
realtimedata-streamolapapachedatastore
navina/pinot
Dec 2021 - Sep 2023
Apache Pinot (Incubating) - A realtime distributed OLAP datastore
Contributions:248 pushes, 46 branches in 1 year 9 months
incubatingrealtimeolapapachedatastore
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