Brandt Barretto

I’ve spent most of my career working on systems that don’t behave the way they’re supposed to, then turning that chaos into tooling. I dig in, figure out what’s actually happening, and build tools so the next person doesn’t have to go through the same pain.

Over the past several years, I’ve leaned heavily into Python-based tooling, observability, and reliability work across distributed AWS systems. At LexisNexis Legal & Professional (RELX), I built Python utilities that correlate CloudWatch metrics, service activity, and infrastructure events to analyze autoscaling behavior—becoming go-to tools during incident analysis. I also led disaster recovery exercises for 15+ critical services, rebuilt AWS environments, and validated recovery timelines.

I’m motivated by production debugging end-to-end: tracing problems through APIs, infrastructure, and data layers, and reducing operational friction with credential helpers, log analysis scripts, and data correlation tools. I bring a strong engineering foundation—backend APIs, REST APIs, Terraform-based infrastructure-as-code, and performance optimization—plus hands-on experience with AI-assisted development tools like GitHub Copilot and LLM-based assistants for coding, refactoring, testing, and documentation.