Steve Tarzia
Director Of Engineering at MongoDB
Chicago, Illinois, United States
Summary
👤
Senior🎓
Top SchoolSteve Tarzia is a Senior Staff Engineer and hands-on technical leader with 17 years building and optimizing high-scale data systems, currently driving query engineering at MongoDB in Chicago. He blends deep database internals expertise—evidenced by contributions to MongoDB core and Netflix's Suro Kafka sink—with leadership experience running distributed, cross-continental engineering teams and academic teaching. His background spans building petabyte-scale analytics storage and indexing at Ocient, founding data-focused nonprofits and startups, and shipping production-grade Java and C++ systems. Notably, he implemented robust Kafka partitioning and overflow-safe counters for Netflix's Suro and resolved subtle aggregation and sharding bugs inside MongoDB, showing a career-long focus on correctness and performance.
17 years of coding experience
12 years of employment as a software developer
Doctor of Philosophy (PhD) Computer Engineering, Doctor of Philosophy (PhD) Computer Engineering at Northwestern UniversityBS Computer Engineering, BS Computer Engineering at Columbia University
Github Skills (20)
aggregate10
c-language10
data-pipelines10
testing10
databases10
kafka10
java10
mongodb-database10
javas10
pipe10
pipeline10
apache-kafka10
aggregates10
cprogramming-language10
data-pipeline10
Programming languages (5)
Github contributions (5)
mongodb/mongo
Jan 2022 - Jan 2023
The MongoDB Database
Role in this project:
Back-end Developer / Database Engineer
Back-end Developer / Database EngineerContributions:31 commits in 1 year
Contributions summary:Steve primarily contributed to bug fixes and improvements related to the MongoDB database. Their work involved addressing issues in aggregation pipelines, specifically correcting the handling of `$indexOfArray` with duplicate values and resolving problems in projection created during dependency analysis. Additionally, the user addressed a compile failure in a test file and made improvements to the sharded find functionality, along with related test updates and timeout adjustments. These contributions demonstrate expertise in database internals and query optimization.
nosqlc-plus-plusmongodb-databasedatabasemongodb
Netflix/suro
Sep 2014 - Oct 2014
Netflix's distributed Data Pipeline
Role in this project:Back-end Developer
Back-end DeveloperContributions:12 commits in 20 days
Contributions summary:Steve implemented a Kafka sink (KafkaSinkV2) for the Netflix Suro data pipeline, using the new Java-native Kafka producer. They addressed key partitioning logic, improved statistics reporting, and added unit tests. The user refactored the code to include message counters, switching from integers to longs to prevent overflow, as well as updating sample configurations. This work focused on providing a robust and efficient means of streaming data to Kafka.
stream-processingpipelinenetflixdata-sciencebig-data
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