Tejas Ajmera
Senior Software Engineer at LinkedIn
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top SchoolTejas Ajmera is a Senior Software Engineer at LinkedIn’s Big Data org in Mountain View, building front-end infrastructure and shared UI/tooling for large-scale anomaly detection, root-cause analysis and forecasting platforms used across the company. He previously built reusable data-visualization libraries that power LinkedIn’s B2B products and public insights, and he contributes front-end improvements to the notable open-source project Apache Pinot. Tejas balances product delivery with platform-level improvements to speed shipping, while actively coaching, mentoring and interviewing to raise team craftsmanship. His background includes core product engineering at FactSet (C++ and AngularJS) and an MS in Computer Science from Georgia Tech. Known for refactoring complex UIs into reusable components and adding lightweight PubSub and filtering support in production front-ends, he operates at the intersection of data, UI, and performance.
5 years of coding experience
9 years of employment as a software developer
Bachelors of Engineering, Computer Engineering, Bachelors of Engineering, Computer Engineering at D.J.Sanghvi College Of EngineeringMasters of Science, Computer Science, 3.58/4.00, Masters of Science, Computer Science, 3.58/4.00 at Georgia Institute of Technology
English, Hindi, Marathi, Gujarati
Github Skills (8)
javascript10
javascripts10
front-end-development10
ember-js10
api9
html9
css9
apim9
Programming languages (1)
Github contributions (3)
apache/pinot
Nov 2020 - Feb 2021
Apache Pinot - A realtime distributed OLAP datastore
Role in this project:
Front-end Developer
Front-end DeveloperContributions:60 reviews, 14 commits, 14 PRs in 3 months
Contributions summary:Tejas primarily contributed to the front-end development of the Apache Pinot real-time datastore. Their work involved refactoring components for detection health and stats, including moving static HTML inside the component and optimizing code. They also built new subroutes for exploring different anomaly types and integrated the Performance Stats API into the front-end. Furthermore, they added support for a lightweight PubSub system and added the filtering support to the table components used in the entity monitoring section of the app.
realtimedata-streamolapapachedatastore
tejasajmera/incubator-pinot
Oct 2020 - Feb 2021
Apache Pinot (Incubating) - A realtime distributed OLAP datastore
Contributions:50 pushes, 15 branches in 3 months
incubatingrealtimeolapapachedatastore
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