Eric Fode is a cloud-native software architect with 16 years of experience building scalable infrastructure and operational practices across startups and enterprises, currently shaping Azure cloud architecture at Bridgewater Associates. He previously led cost-optimization and technical initiatives as a Staff Engineer at CircleCI, delivering a 40% reduction in CoGS within a year, and has principal engineering experience at Oracle and Autodesk focused on serverless and sustainable infrastructure. Eric combines hands-on DevOps and backend skills—contributing bug fixes and tests to flagship open-source projects like NumPy and improving Chef cookbooks for Sensu—with a strong emphasis on test-driven infrastructure, immutable components, and developer workflows. A practitioner of technical writing and team culture-building, he pairs pragmatic engineering with mentorship to align systems to engineers’ and customers’ needs. Based in Oakland, he brings a rare mix of production-scale cloud architecture, cost-conscious execution, and open-source attention to code quality.
16 years of coding experience
12 years of employment as a software developer
Electronics, Electronics at Wenatchee Valley College
Hacker School
Computer Science, Computer Science at Eastern Washington University
Computer Science, Computer Science at Whitworth University
Contributions:26 commits, 1 PR, 5 comments in 2 days
Contributions summary:Eric primarily contributed to the Sensu Chef cookbook by updating various configuration files and recipes. They frequently modified files related to data bag interactions for configuration, particularly involving the use of helpers and deep merging. The contributions span multiple areas, including attributes, recipes, providers, and metadata. These updates involved modifying data bag keys and parameters used within the Chef cookbook.
The fundamental package for scientific computing with Python.
Role in this project:
Back-end Developer / QA Engineer
Contributions:10 commits in 8 months
Contributions summary:Eric primarily contributed to the NumPy library by fixing bugs and improving code quality. They addressed an issue related to the `min_scalar_type` function, ensuring it correctly handled unsigned long long integers and added corresponding regression tests. The user also fixed style issues and whitespace, demonstrating a focus on code readability and maintainability within the project. Additionally, the user added tests for the power function to cover different data types.
lapackpythonmpindarrayconvolution
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