About Me
I'm a designer who cares more about shipping the right product than shipping the shiniest artifact. Before anything gets built, I love to roll up my sleeves and understand the whole experience, rather than just optimizing one step in front of me.
I've spent the last decade turning complex, ambiguous problems into interfaces people actually trust. Right now, I'm leading design for developer experience and security/compliance tooling, audit logs, governance workflows, user management, and the kind of behind the scenes systems that often only get noticed when they don't work. This is my favorite kind of design work: High stakes, technical, and built for people who expect precision from every interaction.
That said, my path here wasn't linear. I started out in graphic design and marketing, which taught me to think about brand, visual clarity, and audience before I ever built a user flow. Later, I went back for an MA in human computer interaction because I wanted the research and systems thinking chops to match the instincts I'd already built. The combination of visual craft plus structured, data-informed problem solving is still how I approach every project.
I partner closely with product engineering, and I care most about the unglamorous details, the edge cases, accessibility gaps, the moments where trust decided built or lost.
A quick note on working lean in the AI era: To state the obvious, things move faster now. I sometimes review five to six complex RFCs a week for experience impact versus maybe one every couple weeks a few years ago. While some days, it feels like I'm putting out fires, the upside is real. For first time product, design, and engineering are actually working in parallel on the same problem in real time, something I spent years trying to figure out how to make happen and never quite could.
A few things that keep this from turning into chaos.
I never let AI define the problem for me directly. While AI enables me to synthesize broader context at the top of them funnel, it's not great at knowing what actually matters.
I don't skip the fundamentals. Archetypes, jobs to be done, etc. These artifacts are what keep a project tethered to real value instead of drifting into AI generated noise.
I always keep iterating after launch. With more rapid releases comes greater responsibility to measure success and iterate further on any gaps.
I'm a designer who cares more about shipping the right product than shipping the shiniest artifact. Before anything gets built, I love to roll up my sleeves and understand the whole experience, rather than just optimizing one step in front of me.
I've spent the last decade turning complex, ambiguous problems into interfaces people actually trust. Right now, I'm leading design for developer experience and security/compliance tooling, audit logs, governance workflows, user management, and the kind of behind the scenes systems that often only get noticed when they don't work. This is my favorite kind of design work: High stakes, technical, and built for people who expect precision from every interaction.
That said, my path here wasn't linear. I started out in graphic design and marketing, which taught me to think about brand, visual clarity, and audience before I ever built a user flow. Later, I went back for an MA in human computer interaction because I wanted the research and systems thinking chops to match the instincts I'd already built. The combination of visual craft plus structured, data-informed problem solving is still how I approach every project.
I partner closely with product engineering, and I care most about the unglamorous details, the edge cases, accessibility gaps, the moments where trust decided built or lost.
A quick note on working lean in the AI era: To state the obvious, things move faster now. I sometimes review five to six complex RFCs a week for experience impact versus maybe one every couple weeks a few years ago. While some days, it feels like I'm putting out fires, the upside is real. For first time product, design, and engineering are actually working in parallel on the same problem in real time, something I spent years trying to figure out how to make happen and never quite could.
A few things that keep this from turning into chaos.
I never let AI define the problem for me directly. While AI enables me to synthesize broader context at the top of them funnel, it's not great at knowing what actually matters.
I don't skip the fundamentals. Archetypes, jobs to be done, etc. These artifacts are what keep a project tethered to real value instead of drifting into AI generated noise.
I always keep iterating after launch. With more rapid releases comes greater responsibility to measure success and iterate further on any gaps.