Summary
Pavan Damalapati is a software engineer focused on distributed databases and machine learning with nine years of experience building high-volume data systems that power ML and data science applications. He has deep backend experience from Hevo and Celonis, working with Kafka, multiple OLTP/OLAP databases, and server frameworks to optimize ingestion, scheduling, and error classification at scale. As a Columbia MS student and graduate research assistant in the Wu Lab, he co-authored papers and helped build tools for training tree-based models on SQL and visualizing complex joins—work that bridges systems research and practical tooling. He routinely turns ambiguous requirements into product wins (e.g., first-to-market Firebolt integration and an 8x ingestion improvement) and automates operational workflows so non-engineers can safely manage errors and costs. He also documents experiments and learnings on his technical blog, signaling a strong habit of communication and reproducible engineering.
9 years of coding experience