About Me

I didn't start as a developer. I studied civil engineering, drawn to the idea of building things that last. But somewhere along the way I discovered that software offers something even more compelling—we can build anything, iterate in hours not years, and share it with the world instantly.

I started with data science. The math drew me in, and data felt like the currency of the decade. Courses on Coursera and Udemy got me going, but real depth came from documentation, technical blogs, research papers, and coursebooks I worked through cover to cover. I built the mathematical foundation first—linear algebra and statistical learning—then moved into the practical side: data wrangling, visualization, and exploratory analysis. Databases came next, both relational and non-relational, including the modeling patterns behind schemas that scale. From there I went deeper into machine learning and deep learning: balancing model complexity against accuracy, benchmarking approaches, training models to avoid over- and underfitting, and building proper data pipelines.

Programming had crept in along the way. By the time I started job hunting in data science, I'd picked up enough full-stack basics that—unexpectedly—I landed a software engineering role instead. That pivot opened a new world. I went deep on the fundamentals: data structures and algorithms, OOP, AOP, SOLID, and the design patterns that let me move between codebases without losing my footing. I didn't just study them—I argued for them in code reviews, brought them to the team where they fit, and mentored juniors along the way.

A few years in, the lesson that kept resurfacing was that scalability and maintainability are what separate code that works from code that lasts. I started studying software architecture seriously—DDD, CQRS, event sourcing, microservices—and applying those patterns in production where they earned their keep.

Most recently, I've been moving into infrastructure. I built a homelab to learn what my day job doesn't quite cover: QEMU/KVM virtualization with libvirt, declarative provisioning with Terraform, configuration automation with Ansible, and end-to-end CI/CD pipelines built on GitLab, GitHub Actions, Jenkins, and Kubernetes. The pattern hasn't changed since I started: find the next thing I don't understand, and go deep until I do.

I believe in learning in public, building systems that are simple enough to reason about, and writing documentation that treats readers as intelligent adults.