Product Manager · Seattle, WA
I build products and think in systems. My background spans backend engineering and enterprise software, giving me the technical depth to engage meaningfully in design decisions and the product instinct to translate complexity into things people actually use.
Background
I started my career as a backend engineer building systems like AML transaction monitoring, financial data pipelines, and compliance infrastructure for global banks. That foundation taught me to think in systems, not features.
I moved into product deliberately. I wanted to own the problem, not just the solution. The shift from Software Engineer to Product Owner to Product Manager was a choice I made each time I felt too comfortable.
Today I am focused on platform and technical product roles at companies building at scale. I bring engineering depth, a bias for evidence over intuition, and a genuine curiosity about how AI can make everyday work faster and less frustrating.
Career
Work
What it does
Pulls real user discussions from Reddit, Hacker News, and DEV.to and uses Claude to compare two competing products side by side — sentiment scores, top praises, pain points, and competitive insights.
Why I built it
I wanted to ground competitive analysis in actual user voice rather than assumptions. Applied it to Microsoft's ecosystem to identify unmet user needs and surface product opportunities.
📄 Case Study
A competitive intelligence study applying the engine to Microsoft's ecosystem — Edge AI vs Chrome AI and Teams vs Slack. Covers methodology, findings, and product recommendations grounded in real user data.
What it does
Generates a rigorous, product-specific metrics framework — north star, three leading input metrics ranked by criticality, guardrail metrics, and the vanity metrics to avoid.
Why I built it
Metrics was the area I knew I needed to get sharper on. I studied the framework deliberately then built a tool that forces me to apply it every time while stopping me from defaulting to vanity metrics.
📄 Case Study
Covers why the tool was built, the five-layer metrics framework it generates, and the shift from defaulting to vanity metrics to defining north stars grounded in real user value.
What it does
An AI-powered tool that structures thinking for high-stakes, ambiguous decisions. It clarifies context, surfaces assumptions, and lays out options without making the decision for you.
Why I built it
Job searching surfaces decision fatigue in ways I did not anticipate. Choosing between opportunities, roles, and tradeoffs under financial pressure needed structure, not more advice. The tool forces clarity before conclusions.
What it covers
A deep analysis of Stripe's API design philosophy, ecosystem extensibility strategy, and platform metrics. Includes an original product recommendation for reducing developer onboarding friction inside test mode.
Why I wrote it
I came at Stripe as a former backend engineer who built the financial systems Stripe abstracts away. That perspective gave me a specific lens on what they got right and why their moat is harder to replicate than most people think.
What it covers
An analysis of how Amazon's seller platform balances ecosystem scale with buyer experience. Covers seller onboarding, the platform accountability paradox, analytics fragmentation, and the two metrics that tell you whether the platform is actually healthy.
Why I wrote it
Marketplaces are one of the most complex platform types to get right. They serve two distinct users simultaneously and have to balance their needs constantly. Studying Seller Central was a way to understand how a platform at massive scale makes that tradeoff, and what a buyer-first north star looks like in practice across every product decision.
Get in touch
I am actively looking for platform and technical PM roles at companies building at scale. If you are building something that needs a PM who can sit in a technical design review in the morning and a stakeholder strategy session in the afternoon, I would love to connect.