Ricky Stewart is an experienced infrastructure and build-focused software engineer with 12 years in production environments, currently shaping developer infrastructure at Cockroach Labs. He has a strong track record at major tech firms including Google, Mozilla, and Microsoft, and contributes to prominent open-source projects such as CockroachDB where he enhanced CI pipelines, build targets, and test tooling. Comfortable across back-end and DevOps responsibilities, he’s improved test execution, result extraction, and SQL-related tooling in projects like sparkmagic. Based in Chicago with a Computer Science BA from the University of Chicago, he combines deep systems know-how with practical tooling improvements that streamline developer workflows. A detail-driven engineer, he often surfaces reliability gains that quietly accelerate team velocity.
13 years of coding experience
5 years of employment as a software developer
Bachelor of Arts (B.A.), Computer Science, Bachelor of Arts (B.A.), Computer Science at The University of Chicago
CockroachDB — the cloud native, distributed SQL database designed for high availability, effortless scale, and control over data placement.
Role in this project:
Back-end & DevOps Engineer
Contributions:1568 reviews, 707 commits, 1673 PRs in 2 years 2 months
Contributions summary:Ricky's commits focused on the development and maintenance of the CockroachDB codebase. They primarily contributed to the build infrastructure, adding features to the continuous integration (CI) pipeline and the tooling to support it. The user implemented various build targets and added integration tests for different components. Furthermore, they worked on test execution, creating mechanisms for test results extraction, test filtering, and error reporting.
Jupyter magics and kernels for working with remote Spark clusters
Role in this project:
Back-end Developer
Contributions:108 commits in 3 months
Contributions summary:Ricky primarily contributed to the development of the Spark magic and Livy client libraries. The changes included the addition of new result types, fixing result rendering methods, and improving parsing of SQL query results. The user also refactored the code to use SQL queries in the pyspark client and implemented SQL query sampling options.
jupytersparkkernelclusterlivy
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