My background spans field science, research, and production environments where data is messy and outcomes matter.

I started in remote field and lab roles, where reality has a way of breaking clean plans. That foundation shaped how I build systems: stay grounded in context, document decisions, and design workflows that hold up when assumptions fail. It also taught me that delivery depends on people as much as tools, and that clarity matters most when things change.

Since then, my work has centered on building reproducible pipelines and turning complex inputs into outputs others can rely on. I focus on standardizing and automating repeat work so outcomes are delivered consistently without heroics.

Across all of this, I’ve learned that technical problems don’t stay technical for long. Data only becomes meaningful once it’s interpreted, explained, and used, and the quality of a system is measured by how well it holds up in the hands of the people who depend on it.