Deepthi Srinivasan
About Deepthi Srinivasan
Deepthi Srinivasan is a Software Engineer at Yugabyte, where she has worked since 2020, focusing on YSQL's DDL Propagation and Row Level Geo Partitioning features. She holds a Bachelor's degree from Anna University and a Master's degree from North Carolina State University, and has previous experience at EMC, Nutanix, and Tintri.
Work at Yugabyte
Deepthi Srinivasan has been employed at Yugabyte as a Software Engineer since 2020. She is based in Sunnyvale, California, where she works on various projects related to database technology. Her responsibilities include developing sub-features in YSQL's DDL Propagation, which allows for seamless schema changes across nodes in a cluster. Additionally, she plays a significant role in the development of the Row Level Geo Partitioning feature, which improves data residency management at the table-row level.
Education and Expertise
Deepthi Srinivasan holds a Bachelor of Engineering (BEng) in Computer Science from Anna University, where she studied from 2008 to 2012. She further advanced her education by obtaining a Master's degree in Computer Science from North Carolina State University, completing her studies from 2014 to 2016. Her academic background supports her specialization in low latency multi-region deployments and transactional consistency semantics.
Background
Before joining Yugabyte, Deepthi Srinivasan worked at several technology companies. She began her career at EMC as an Associate Software Engineer from 2012 to 2014 in Bangalore, Karnataka, India. She then transitioned to Nutanix, where she served as a Senior Member of Technical Staff from 2016 to 2020 in San Jose, California. Additionally, she gained experience as a File Systems Intern at Tintri for three months in 2015 in Mountain View, California.
Achievements
Deepthi Srinivasan presented a talk on Row Level Geo-Partitioning in YugabyteDB on September 23, 2022. This presentation highlighted her contributions to the development of features that enhance database functionality and performance, particularly in the context of data management across distributed systems.