GTM Engineering is the core of my practice — alongside RevOps. Read more about my approach →
GTM Engineering

Build the machine that multiplies your sales team

Enrichment pipelines, signal-based outbound, AI agents, and custom workflows. The work that lets a team of 6 perform like a team of 15. Not by working harder — by building better systems.

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What I can do for you

GTM Engineering is the discipline where technical builders design and operationalize revenue systems. I combine go-to-market knowledge with the technical skills to build those systems: data engineering, API integrations, AI orchestration, and automation. The outcome: your revenue team delivers the output of a team many times its size — without extra hires.

💧 Waterfall enrichment

From 20% to 80%+ coverage by chaining Clay waterfalls with LeadMagic, Findymail, and Apollo. Pay-per-success pricing. Read: the waterfall stack.

📡 Signal-based outbound

Detect buying signals via Common Room, Trigify, and custom flows. Reply rates from 1-3% up to 8-15%. Read more.

🤖 AI agents & MCP

Pre-call research agents, lead classifiers, churn predictors. Production-ready with Claude/GPT-5 via MCP. See GTM in the age of AI.

🏗️ CRM architecture

HubSpot or Salesforce builds that scale with you. Custom objects, programmable workflows, integrations. For heavy HubSpot builds I bring in Pack of Nodes.

📊 Lead scoring & routing

Fit + intent scoring models, real-time routing, MQL/SQL pipeline automation. No more manual assignment.

🔄 Workflow orchestration

n8n, Make, Clay, and custom Python services that make your revenue stack work as one. No more point-to-point spaghetti.

The three forces that make GTM Engineering essential

The GTM Engineer role emerged from three simultaneous trends:

  1. CAC is rising structurally. Median B2B SaaS payback went from 11 months (2021) to 18 (2026). VCs demand under 12 for good term sheets. Hiring more SDRs isn't the answer.
  2. AI made personalization at scale possible. What used to take 15 minutes per prospect now takes an agent seconds. But the value isn't in the AI — that's commoditized — it's in the data and orchestration around it.
  3. The Clay-Cargo-Common Room generation. Programmable GTM data platforms require someone who can operate them at developer level, not just click through a UI.

GTM Engineering versus RevOps

GTM Engineering is Build: designing new systems, running experiments, prototyping. RevOps is Run: governance, maintenance, optimization of existing systems. Two sides of the same coin.

For scale-ups under €15M ARR, one partner doing both is more efficient than two separate teams. I lay the governance foundation and build the systems in parallel. That way you build on a healthy base from day one instead of having to repair things later. More on this: GTM Engineering and RevOps together.

My approach

I start with an audit of your current GTM stack: what's running, what's manual, where are the biggest levers? That feeds a 90-day roadmap with one big build (typically waterfall enrichment or signal-based outbound), running in parallel with the RevOps foundation. By the end of the quarter you have working systems, not a deck full of recommendations.

I often work 1-2 days per week as interim GTM Engineer. The systems I build can be maintained afterwards by a junior or mid-level hire. For deep HubSpot implementations I bring in Pack of Nodes for the pure HubSpot engineering.

When is GTM Engineering right for you?

Three signals that it's time:

How does your GTM engine score?

Take the free GTM Scan and find your biggest lever. Personal advice in 10 minutes.

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