Nehil Jain
SF Bay Area, India
Summary
👤
SeniorNehil Jain is a seasoned data-driven software engineer and technical leader based in the San Francisco Bay Area with 12+ years delivering data-intensive platforms. He is currently a Member of Technical Staff at Anyscale, where he blends hands-on backend engineering with AI-focused infrastructure to scale large-scale experimentation. A former McKinsey/QuantumBlack technology expert and co-founder in stealth AI, he has led data engineering and platform initiatives across enterprises and startups, including building data pipelines, serverless architectures, and analytics dashboards. An active open-source contributor, he significantly enhanced the great-expectations data validation framework by adding CLI notebook launchers, tests, and documentation, boosting data exploration capabilities. He previously built data ecosystems for Athletigen and academia, and earned an MSc in Mathematics and a BE in Electrical & Electronics Engineering from BITS Pilani, underscoring a strong mathematical and systems foundation. Passionate about GenAI and 0-to-1 product development, he thrives on turning complex data problems into practical, scalable solutions.
13 years of coding experience
Stackoverflow
679reputation
73kreached
8answers
7questions
Github Skills (23)
python10
commandline-interface10
command-line-interface10
commandline10
command-line10
cli10
testing9
jupyter-notebook9
documentations8
documentation8
data-engineering8
data-science7
data-quality7
data-profiling7
sql6
Programming languages (14)
Github contributions (5)
great-expectations/great_expectations
Apr 2020 - May 2020
Always know what to expect from your data.
Role in this project:
Back-end Developer & Automation Engineer
Back-end Developer & Automation EngineerContributions:8 commits, 1 PR, 8 comments in 6 days
Contributions summary:Nehil contributed to the project by adding functionality for launching Jupyter notebooks through the command line interface, enhancing the project's utility for data exploration and analysis. They also made changes to the data context and dataset modules, likely to improve data handling capabilities. Additionally, the user added tests for the command-line interface toolkit, improving test coverage. Furthermore, they updated documentation, enhancing the usability of the project.
pythondatadata-integritydatacleanerpipeline-testing
nehiljain/dotfiles
Nov 2016 - Feb 2023
:wrench: .files, including ~/.macos — sensible hacker defaults for macOS
Contributions:63 pushes, 1 branch in 6 years 3 months
sensiblekarabinerasdfwrenchstow
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