Kevin Knight
Vice President, Engineering at Goldman Sachs
Asheville, North Carolina, United States
Summary
🤩
Rockstar🎓
Top SchoolKevin Knight is a Vice President of Engineering with 13 years in data engineering at Goldman Sachs, leading development of a data modeling platform from Asheville, NC. He brings deep technical roots in AI and knowledge representation from earlier roles at Cycorp and research engineering in socio-cognitive modeling at the University of Pennsylvania. His hands-on back-end work includes meaningful contributions to the finos/legend-engine project, where he improved core compilation and model-processing components—evidence of both systems-level thinking and compiler-style expertise. Kevin holds a PhD in Mathematics and a broad liberal-arts foundation in math and philosophy, blending rigorous formal training with practical software delivery. Colleagues rely on him to translate complex, theory-heavy problems into production-ready platforms that serve large financial datasets. An unusual thread through his career is modeling real human cognition and trust, which informs his pragmatic approach to building systems that reflect how people actually behave, not just how models assume they should.
13 years of coding experience
8 years of employment as a software developer
PhD, Mathematics, PhD, Mathematics at The University of ManchesterLaw, Law at New York University School of LawBA, Mathematics, Philosophy, Applied Mathematics, BA, Mathematics, Philosophy, Applied Mathematics at Rice University
Github Skills (6)
legend10
javas10
compiler-design10
modeling10
java10
testing8
Programming languages (3)
Github contributions (5)
finos/legend-engine
Oct 2020 - Jan 2023
Legend Engine module
Role in this project:
Back-end Developer
Back-end DeveloperContributions:136 reviews, 31 commits, 147 PRs in 2 years 3 months
Contributions summary:Kevin primarily focused on enhancing the Legend Engine module. Their work involved significant modifications to core components such as `PureModelContextData` and `CompileContext`, indicating a strong understanding of the system's internals. They also implemented and improved compiler extensions, added license, and refactored code to improve performance. These changes suggest a focus on the engine's compilation and processing logic.
modelinglegend
kevin-m-knight-gs/legend-sdlc
Oct 2020 - Mar 2025
Legend SDLC module
Contributions:656 pushes, 158 branches in 4 years 6 months
pythonlegendsdlc
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