Ravi Aringunram
Director - Google Cloud Storage (Efficient Fleet)
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top SchoolRavi Aringunram is a seasoned engineering leader with 20+ years of experience building and operating large-scale distributed systems for search, analytics, and cloud storage. Currently directing Google Cloud Storage Efficiency and Data Orchestration, he leads optimization of exabytes of storage and global data movement while previously heading metadata and serving systems that manage trillions of blobs. His background spans leadership roles at LinkedIn (running real-time OLAP with Apache Pinot), Microsoft (Bing indexing and Cosmos), and eBay, combining deep hands-on backend engineering with people management of large, cross-functional teams. Ravi is comfortable translating product needs into scalable architectures—frequently in high-throughput, low-latency domains—and contributes to open-source OLAP work, notably enhancing Apache Pinot’s validation and monitoring capabilities. He holds advanced technical training and executive education from the University of Florida and Stanford GSB’s LEAD program, blending technical depth with strategic operational experience.
11 years of coding experience
20 years of employment as a software developer
LEAD Program, LEAD Program at Stanford University Graduate School of BusinessBachelor of Engineering, Computer Science and Engineering, Bachelor of Engineering, Computer Science and Engineering at Shanmugha Arts, Science, Technology and Research AcademyMaster of Science, Computer Science and Engineering, Master of Science, Computer Science and Engineering at University of Florida
Github Skills (7)
javas10
apache-pinot10
java10
metric10
testing9
validation9
validations9
Programming languages (2)
Github contributions (3)
apache/pinot
Jan 2016 - Jun 2016
Apache Pinot - A realtime distributed OLAP datastore
Role in this project:
Back-end Developer
Back-end DeveloperContributions:13 commits, 4 PRs, 3 pushes in 4 months
Contributions summary:Ravi primarily focused on enhancing the Apache Pinot data store by adding features and improving existing functionalities. Their work included updating Javadoc comments, adding metrics for validation and controller errors, and implementing code to compute document counts for both offline and real-time segments. Additionally, the user contributed to test improvements and addressed review comments on document count calculations. These commits demonstrate a focus on improving data validation and monitoring capabilities within the Pinot system.
apache-pinotolapjava
raviarin/NavSharedApp1
Mar 2015 - Mar 2015
Contributions:1 branch in 1 day
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