Product Leadership for Business Outcomes
I build and scale technical products where success is measured in revenue impact, efficiency, and durable leverage—not feature counts or vanity metrics.
Building Systems That Move the P&L
I’m a Principal Technical Product Leader at Amazon, leading large-scale automation and platform work at the intersection of customer discovery, measurement, and go-to-market systems.
I build and scale technical products where success is measured in revenue impact, efficiency, and durable leverage—not feature counts or vanity metrics.
Automation, Measurement, and Leverage
My work sits in the messy middle between strategy and systems: turning ambiguous growth goals into operating models, metrics, and scalable tooling that teams can actually run.
I focus on compounding impact—clean inputs, reliable measurement, and automation that reduces manual work while increasing performance. If it can’t be measured, trusted, and maintained, it doesn’t ship.
This site is where I share practical patterns for building advertising and marketing systems like products—so outcomes improve even when teams, tools, and platforms change.
Operating at the Intersection of Giants
My work spans external platform ecosystems. I’ve partnered with some of the largest technology platforms in the world to shape and launch roadmap capabilities.
That means I regularly operate in B2B environments where multiple roadmaps, incentives, and technical constraints must align—translating between executive goals, platform capabilities, and real-world implementation.
From Healthcare AI to Global Commerce
Before Amazon, I managed and led product, engineering, and science teams building healthcare platforms and conversational agents at Oracle Health—well before AI assistants became mainstream.
That experience shaped how I think about applied AI, regulated environments, and the difference between technology that demos well and systems that actually get adopted.
AI is rarely the strategy. Strategy is the strategy.
Working on conversational systems in healthcare before the current AI wave made one thing clear: AI becomes powerful only when paired with clear objectives, quality inputs, and operating models that people trust.
The difference between AI that creates leverage and AI that becomes shelfware is almost always product judgment.
What You’ll Find Here
Connecting roadmaps to measurable business outcomes
Prioritization under real technical constraints
Metrics that clarify direction instead of obscuring it
Frameworks for decisions with incomplete information
Patterns from building at enterprise and internet scale
Practical AI integration without the hype
Ready to Build Systems That Scale?
No cargo-cult playbooks. No inflated jargon. Just patterns that hold up under real load.
The views expressed here are my own and don’t represent Amazon or any other employer.