About
GTM engineer. Foundations-first.
Biography
I've spent about twenty years in marketing operations and GTM systems, usually as the first person in the door. The one who builds the stack, the data model, and the process from scratch, gets it running, then hires and hands it off. B2B software, early-stage through public.
Along the way I got good at a less popular skill: telling people what not to build. The market is loud in both directions right now. One camp says AI does everything, the other says it's all a bubble. Both are half right. I care about one question instead: does this produce value that compounds, that you can measure, that you actually own? If it doesn't clear that bar, I'll say so, even when it costs me the work. The willingness to say no is the whole point.
I went independent to do this the way I think it should be done: outcome-first, vendor-neutral, hands-on with the build instead of handing you a deck. AI made building cheap. It didn't make owning cheap. That gap is where I work.
The short version
What nearly two decades in GTM operations actually produces.
- Nearly 20 years in marketing operations Solo builder to team leader, across early-stage through public B2B software. Started as the first hire, built the stack, then hired to grow it. In-house practitioner every time, not an agency rep.
- End-to-end GTM systems ownership Data model, attribution, pipeline ops, and tooling architecture across the full stack, connected end to end.
- Hands-on agentic and AI build capability Building with current-generation AI tools, not just evaluating them. Agents, integrations, workflows that run in production and get handed off.
- Calibration as a practice Judgment about what is worth building before anything gets built. The bar: does it compound, can you measure it, do you own it? If it doesn't clear that, skip it.
I build the things that compound, that you can measure, that you own. The rest, I'll talk you out of.
The calibration stance
Not every AI problem is worth building.
Calibration is a filter, not a stance on the technology. It's asking whether the build will hold up at scale, whether the data can carry it, and whether the outcome actually compounds. Most GTM teams skip that question and buy the outcome of it later, in the form of systems nobody trusts.
Impact themes
The patterns that show up across the work.
Recurring outcomes from builds across GTM and marketing ops, not a highlight reel.
Systems and processes that last
I've designed and implemented multi-million dollar martech stacks and marketing data infrastructure that kept running long after the leadership that scoped it moved on.
Sprawl into something runnable
Turned half-integrated, overlapping stacks into systems a team could actually operate.
Strategic AI implementation
Built AI into live GTM workflows that scaled beyond proof-of-concept.
Signal architecture, end to end
Took PLG and PQL signal models from idea to instrumented pipeline.
Speaking & Workshops
Available for conferences, workshops, and team sessions.
I also speak and run workshops on this. Speaking & workshops
See speaking details