I specialize in solving the messy problems that come with scaling software products internationally. Right now at Broadway Media, I'm dealing with multi-region delivery challenges: how do you build a platform that handles different tax regulations, localization requirements, and performance expectations across continents? The answer usually involves type-safe abstractions that prevent configuration errors before they hit production.

One pattern I've found consistently valuable is creating unified registry systems for complex domains. When you're managing multiple interconnected resources with hierarchical relationships, the temptation is to build custom logic for each one. Instead, I build generic interfaces with strong TypeScript constraints that make invalid states impossible to represent. This cuts boilerplate dramatically and makes onboarding new engineers straightforward because they learn one pattern that applies everywhere.

The technical decisions that matter most aren't about picking the shiniest framework. They're about understanding where your bottlenecks actually are. At Vodacom, the problem wasn't our backend performance, it was that our frontend framework made debugging impossible and deployments painfully slow. Moving to React in iFrames wasn't elegant architecture, it was solving the real problem: developer velocity. Sometimes the best solution is the one that removes friction from your team's workflow rather than the one that looks cleanest on paper.

I've led teams ranging from three engineers to managing cross-functional stakeholders at early-stage startups. The common thread is identifying systemic issues rather than treating symptoms. When a team is struggling, it's rarely about individual skill. It's usually missing guardrails, unclear patterns, or technical debt that's ground morale down. At BSure, implementing automated linting and Git hooks didn't just catch bugs, it created a feedback loop that taught junior developers better practices automatically.

My technical stack centers on TypeScript, Node.js, React, and AWS, with recent work integrating AI for workflow automation. But the real value is pattern recognition from working across product lifecycles, from founding startups to enterprise delivery. I spot problems early because I've seen how they compound later.