Paul Hemberger
Principal Software Engineer at HubSpot
Greater Boston United States
Summary
🤩
Rockstar🎓
Top SchoolPaul Hemberger is a Principal Software Engineer based in Greater Boston with 13 years of experience building and operating large-scale distributed systems and data infrastructure. He has deep hands-on expertise in databases, real-time data integration, and SQL query planning, evidenced by contributions to Materialize where he improved the SQL parser, time zone handling, and Kafka/Postgres ingestion performance. At HubSpot he led efforts on MySQL/Vitess at massive scale and upgraded one of the world's largest Elasticsearch clusters, focusing on reliability, operational sustainability, and reducing customer-facing outages. A proven engineering leader, he has moved between IC and management roles—most recently VP of Engineering at Materialize—while continuing to ship code and mentor teams. He combines rigorous MIT EECS training with a practical obsession for correctness (particularly around race conditions and query semantics). Notably, he blends product-minded operational improvements with deep compiler- and datastore-level work that few infrastructure engineers span end-to-end.
13 years of coding experience
13 years of employment as a software developer
Master of Engineering (M.Eng.), Electrical Engineering & Computer Science, Master of Engineering (M.Eng.), Electrical Engineering & Computer Science at Massachusetts Institute of Technology
Github Skills (12)
postgresql10
sql10
rust9
kafka8
avro8
admin7
data-engineering7
database7
databases7
stream-processing7
database-administration7
distributed-systems6
Programming languages (11)
Github contributions (5)
MaterializeInc/materialize
Jan 2022 - Jan 2023
The live data layer for apps and AI agents. Create up-to-the-second views into your business, just using SQL
Role in this project:
Backend Developer
Backend DeveloperContributions:686 reviews, 108 commits, 346 PRs in 1 year
Contributions summary:Paul's commits primarily focus on enhancing Materialize's SQL parser and related features. This involves handling time zones, creating Avro schema parameters for Kafka sinks, and refining SQL query planning, specifically for GROUP BY clauses. They are also involved in improvements for interval handling and in the data ingestion from Postgres (pg-cdc), specifically refactoring to use `COPY TO` for performance reasons.
ai-agentdatalayersqlrustdatabase
obi1kenobi/pyre
Nov 2013 - Dec 2013
Contributions:4 commits in 19 days
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