Approach
Every technical problem has architecture underneath it.
The interesting work isn't choosing tools — it's making the decisions that shape how systems behave over time. I've spent my career at the intersection of strategy and implementation, where understanding why a system should exist matters as much as knowing how to build it.
Systems
The most valuable technical work happens before anyone writes code.
Technology decisions are business decisions. Budget, risk, ROI, compliance, vendor lock-in — these aren't afterthoughts; they're the actual constraints that architecture has to account for. Seven years as the sole technologist for a digital agency taught me to think in those terms first and reach for tools second.
Architecture reviews. Build-vs-buy evaluations. Infrastructure cost modeling. Technical due diligence that translates directly into confident decision-making for non-technical leadership. That's the work I keep coming back to.
Platforms
Complex platforms fail from governance gaps, not technical limitations.
I learned Salesforce from zero, executed a 32,000+ metadata change migration across five integrated systems, and stayed to fix the architecture underneath. That pattern — learn fast, ship production results, build the governance that prevents the next mess — is how I approach any complex platform ecosystem.
Three years of stalled progress resolved in months. The migration itself was the easy part. The harder work was designing the release management, data governance, and integration patterns that kept it from drifting back.
Web
The web should be fast by default.
Sites score 95–100 on Lighthouse because the architecture decisions are right, not because someone ran an optimization pass after the fact. Static-first. Edge-deployed. Structured for search engines from day one. I build on Astro, Tailwind, and Cloudflare because the stack is deliberate — not because it's trending.
This site is the proof. Sub-second load times. Perfect SEO scores. Maintained entirely through AI-assisted workflows. If you're curious what that process looks like, the case studies walk through it in detail.
Philosophy
Start with the constraint. Build from there.
Understand first
Every problem starts with listening. What are the actual constraints? Budget, timeline, team capacity, technical debt, compliance. The architecture has to account for all of them — not just the ones that are fun to solve.
Build to last
I design systems where the problem doesn't come back. Not the fastest fix — the right one. Documentation, governance, and knowledge transfer aren't afterthoughts.
Stay curious
Salesforce from zero. Kubernetes from documentation. AI tooling before it was mainstream. The pattern is the same: learn fast, ship production results, then teach what I learned.
See how this plays out in practice.
The case studies walk through real problems, real constraints, and the architecture decisions that resolved them.