Noah Sloan is a Staff Software Engineer and product architect with 16 years of experience building high-throughput, distributed systems and leading small engineering teams. He has driven front-end modernization (React, Vue) and continues to ship full-stack Scala/Java backend work powering a microservice fetch platform that ingests millions of social messages per hour. His day-to-day blends DevOps, mentoring, and hands-on work with Kafka, Cassandra, Elasticsearch, Kubernetes and AWS, and he’s authored and maintained critical production infrastructure at Spredfast. Noah is an active open-source contributor with notable back-end and tooling work in projects like Argo CD Image Updater and kube-state-metrics, reflecting a strong operational focus. He’s comfortable across languages and stacks—PHP through modern JS and JVM—and brings pragmatic architectural judgment from startups to media and security companies. Based in Tulsa, he pairs deep systems expertise with a habit of improving developer workflows and observability rather than just adding features.
16 years of coding experience
20 years of employment as a software developer
MS Computer Science, MS Computer Science at Baylor University
Contributions:3 reviews, 8 commits, 5 PRs in 8 months
Contributions summary:Noah primarily contributed to enhancing the functionality of the Argo CD Image Updater. They implemented features to force image updates and addressed issues related to image updates with specific strategies. Their work also involved refactoring code, adding test cases, and modifying the application's handling of image updates. Furthermore, the user worked on enabling write-back capabilities to Kustomization files.
Add-on agent to generate and expose cluster-level metrics.
Role in this project:
Back-end Developer & DevOps Engineer
Contributions:6 reviews, 14 commits, 1 PR in 2 months
Contributions summary:Noah primarily contributed to the development and configuration of custom resource metrics within the kube-state-metrics project. They introduced the ability to configure custom resource metrics, allowing users to surface specific fields. Additionally, the user refactored the configuration process, integrating inline YAML configuration and flag-based file loading. The changes also included test improvements and the addition of experimental notes.
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