Matthew Cavener is a software engineer with 11 years of experience building production-grade systems across .NET, TypeScript, Python, and cloud-native platforms. Currently at divvyDOSE, he designs REST APIs and ETL functions that turn order data into actionable customer insights, and previously helped Nike generate synthetic test data to improve self-service tooling. His background spans startups and enterprise teams—improving chatbots with data scientists at Carvana, shipping customer-facing web apps on Kubernetes at Nextiva, and automating in-store systems at 7-Eleven—grounded in a physics BS and hands-on research in spectroscopy. Known for a curious, experimental approach, he pairs analytical rigor from scientific research with pragmatic engineering to solve messy, real-world data problems. Based in Portland, Oregon, he thrives at the intersection of data engineering and backend development, often bridging domain knowledge and scalable implementation.
11 years of coding experience
7 years of employment as a software developer
Bachelor of Science - BS, Physics, Bachelor of Science - BS, Physics at Oklahoma State University
Contributions:22 PRs, 33 pushes, 22 branches in 2 months
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