I work across Kubernetes, cloud infrastructure, platform engineering, and systems architecture. Most of my day-to-day sits between product needs and low-level infrastructure behavior — debugging clusters, shaping platform interfaces, writing automation, and documenting the parts that shouldn't need to be rediscovered later.

I write in Go and Python. Go for Kubernetes-native tooling, operators, and control plane work. Python for APIs, automation pipelines, and data workflows. I'm an emeritus contributor to conda-forge, where I worked on the auto-tick bot infrastructure and package maintenance automation.

My background is in applied mathematics — I studied at the Federal University of Santa Catarina with a focus on optimization, nonlinear programming, and computational geometry. My thesis work applied distance geometry and spectral methods to protein structure determination. I retain an active interest in computational neuroscience and optimization.

This site is a collection of field notes, architecture decisions, and debug archive entries. Mostly written for myself. Useful if you're debugging the same things.

focus Platform engineering, Kubernetes, GitOps, identity, ingress, TLS, and infrastructure reliability.
currently Building platform automation with Go at OpenTeams. Debugging bare-metal K3s clusters. Writing notes for problems worth solving only once.
background Mathematics and computer science at UFSC. Optimization, distance geometry, protein refinement. GSoC 2020 with conda-forge.
approach Debug → understand → document. Production friction should produce architecture insight, not just a fix.