About Me

I’m a data engineer with deep expertise in building secure, scalable, open-source data science platforms for government. My background spans from bicycle mechanics to advanced data systems, bringing a craftsman’s approach to digital infrastructure. I specialize in Kubernetes and data engineering to create tools that serve, not rule, with a focus on developing solutions that prioritize user autonomy and digital sovereignty.

Current Focus

I work on cross-functional teams building government-wide, open-source data science platforms using Kubeflow, Kubernetes, and JupyterLab. My work centers on:

• Architecting scalable, secure infrastructure for reproducible workflows
• Mentoring developers in GitOps, CI/CD, and ethical data practices
• Championing open-source adoption within federal constraints
• Building platforms that empower teams to do their best work

Philosophy

I believe in tools that teach rather than obscure; systems that reward curiosity with understanding rather than locking users into black boxes. Whether it’s Kubernetes, bicycle mechanics, or traditional healing arts, I’ve found that the most valuable skills are those that compound over time, offering deeper insights as you invest in understanding.

My approach combines technical rigor with a human-centered perspective, rooted in collaboration, transparency, and resilience. I lead by enabling rather than controlling, building infrastructure that empowers teams while maintaining focus on long-term sustainability and ethical practices.

Technical Expertise

• Platform Engineering: Kubernetes, Kubeflow, JupyterLab, Docker
• Data Engineering: Pipeline design, reproducible workflows, SAS migration
• Infrastructure: Cloud platforms (AWS, GCP, Azure), GitOps, CI/CD
• Languages: Python, R, Golang, SQL
• Specializations: Open source advocacy, digital sovereignty, ethical data governance