Devesh Agrawal
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolDevesh Agrawal is an experienced systems software engineer with 11 years building and improving high-performance database, virtualization, kernel, and storage systems. Comfortable both creating MVPs in startups and driving metrics-led improvements in large-scale legacy products, he has worked on high-performance data analytics and some of the world’s largest big data solutions. He holds advanced CS degrees from IIT Madras and UMass and combines deep academic foundations with practical delivery in early-stage and large-team environments. An active contributor to Apache Superset, he has improved database connectivity and added support for Pinot, demonstrating hands-on expertise integrating complex query engines into analytics platforms. Devesh is motivated to move into technical leadership roles where engineering rigor and customer impact meet.
11 years of coding experience
Indian Institute of Technology MadrasMS, Computer Science, MS, Computer Science at University of Massachusetts
Hindi, English, German
Github Skills (13)
apache-superset10
sqlalchemy10
sql10
python10
postgresql9
database-design9
data-visualization8
data-visualisation8
data-analytics8
data-visualizations8
data-engineering8
flask-ask7
flask7
Programming languages (7)
Github contributions (5)
apache/superset
Jan 2019 - Apr 2019
Apache Superset is a Data Visualization and Data Exploration Platform
Role in this project:
Back-end Developer
Back-end DeveloperContributions:9 commits, 13 PRs, 81 comments in 2 months
Contributions summary:Devesh contributed to the Apache Superset project by focusing on improvements and fixes related to database connections, particularly with Presto and Pinot. They addressed issues with configuration management, logging, and uniqueness constraints within the database schema. Furthermore, the user implemented support for Pinot, including adapting the system for Pinot's specific query language and data handling behaviors, enhancing Superset's data source compatibility. They also improved select control functionality, including supporting arbitrary expressions in groupings.
pythonapache-supersetflaskbusiness-analyticsbusiness-intelligence
agrawaldevesh/spark
Jul 2020 - Jan 2022
Apache Spark - A unified analytics engine for large-scale data processing
Contributions:1 PR, 128 pushes, 12 branches in 1 year 6 months
analyticsdata-processingapachebig-dataspark
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