GTM Engineer Job Description (Copy-Paste Template)
A GTM engineer job description should be scoped around systems ownership, not tool proficiency: owning the signal-to-action loop, building enrichment and scoring pipelines, and automating routing and outbound personalisation. The strongest predictor in hiring is a practical exercise where the candidate designs a data model for a real play, not a tool-based screen.
- Scope the role by the loop it owns — signal, enrich, score, route, act — not by a tool list.
- Naming Clay in the requirements attracts operators. Naming data modelling attracts builders.
- Include the salary band. This role has no stable market rate, and omitting it costs you good candidates.
- Use a paid take-home that mirrors real work: design the model, then defend the trade-offs live.
- Report the role into RevOps or the CRO, never into an SDR team, or it becomes a list-building function.
Before you write the description
Three decisions determine whether this hire succeeds, and all of them happen before the posting goes live.
- Where does it report? RevOps or the CRO. Reporting a GTM engineer into an SDR team reliably turns the role into list building with extra steps. The full sourcing and interview process is in how to hire a GTM engineer.
- Is the CRM trustworthy? GTM engineering is built on top of the system of record. If the data model is broken, the hire will spend their first two quarters doing RevOps work they were not hired for.
- Who owns the commercial strategy? A GTM engineer executes a point of view about who to target and why. If nobody holds that view, they will be asked to invent it, and the role becomes unassessable.
The job description
Copy this and adapt the bracketed sections. It is deliberately specific — vague postings for this role attract a very high volume of unqualified applicants because the title is fashionable.
GTM Engineer
About the role. You will own the systems that decide which accounts our go-to-market team talks to, when, and with what context. This is a building role, not an execution role: your output is working infrastructure — enrichment pipelines, signal detection, scoring models, routing logic, and automated outbound — that produces qualified conversations without a human doing the research.
What you will own.
- The signal-to-action loop end to end: detecting a buying signal, enriching the account, scoring it, routing it, and triggering the play.
- Our enrichment architecture — provider waterfall, coverage, cost per record, and data freshness.
- The fit and intent scoring models, including the feedback loop from closed-won data back into the weights.
- Routing rules and SLA enforcement in the CRM, working with RevOps.
- Personalisation systems for outbound, including LLM-generated research with a validation layer.
- Monitoring and alerting across every automated job, so silent failures surface within hours rather than weeks.
- Measurement of each play: reply, meeting, and pipeline conversion, plus fully loaded cost per qualified opportunity.
What we are looking for.
- You think in data models. You can explain how accounts, contacts, signals, and plays should relate, and where each fact should live.
- You are comfortable in SQL and can validate what a tool tells you rather than trusting it.
- You read API documentation without friction — auth, pagination, rate limits, retries, webhooks.
- You have commercial judgement about which signals indicate real buying intent and which are noise, ideally from having worked close to a sales floor.
- You have built something end to end that other people depended on, and you can describe what broke and how you found out.
- You understand email deliverability well enough to protect a sending domain.
Nice to have. Experience with orchestration platforms such as Clay, warehouse-native GTM tooling, dbt, Python for data work, and prior ownership of an outbound number. Strong candidates frequently arrive through self-directed training rather than a formal background — weight the portfolio, not the route.
How we will measure you. Median signal-to-action latency, qualified contact rate from system-sourced accounts, and fully loaded cost per qualified opportunity. Not workflows built.
Compensation. [$130,000–$170,000] base plus [15%] variable tied to pipeline generated. [Equity band.] [Location and remote policy.]
What to leave out
| Common line | Why it hurts you |
|---|---|
| Must be a Clay expert | Selects for tool operators over system designers. Clay proficiency is learnable in weeks; data modelling is not. |
| Manage and execute outbound campaigns | Conflates the builder with the operator. You will hire an SDR with a technical hobby. |
| 5+ years GTM engineering experience | The title barely predates 2023. You are filtering for people who renamed their LinkedIn headline. |
| Own the CRM | That is RevOps. Bundling both produces a role nobody can do well and everyone blames. |
| Rockstar / ninja / growth hacker | Attracts generalists. This is a precision role. |
The interview process
- 01Screen (30 min) — the systems conversation
Ask them to describe a system they built end to end. Listen for whether they talk about the data model and failure handling, or about the tools. Strong candidates describe what broke and how they detected it.
- 02Practical exercise (paid, 2–3 hours)
Give them a real play: here is our ICP, here is a signal we care about, design the pipeline that turns that signal into a personalised contact attempt. Ask for the data model, the enrichment strategy including cost per record, the scoring logic, and the failure modes. Deliverable is a document, not a working build.
- 03Exercise review (60 min)
Have them defend the trade-offs. Push on cost, on what happens when a provider returns nothing, on how they would know the model was wrong. This session separates candidates more reliably than any other stage.
- 04Commercial interview (45 min)
With a sales leader. Can they explain why a given signal correlates with buying? Do they understand what makes an outbound message land? This is the filter that stops you hiring a data engineer with the wrong instincts.
- 05References
Ask specifically what the candidate built that is still running, and what fell over after they left.
The scorecard
| Dimension | Weight | Strong signal |
|---|---|---|
| Data modelling | 30% | Designs the schema before the workflow; can justify where each fact lives |
| Systems thinking | 20% | Volunteers failure modes and monitoring unprompted |
| Commercial judgement | 20% | Distinguishes signals that predict buying from signals that are merely measurable |
| Technical fluency | 20% | SQL, APIs, and enough scripting to be unblocked |
| Communication | 10% | Explains a technical trade-off to a sales leader without jargon |
Weighting data modelling highest is deliberate. It is the skill that compounds, the hardest to teach, and the one most consistently missing in candidates who present well on tools. Full context on the role in what is a GTM engineer, and on the enrichment layer they will own in waterfall enrichment.
Compensation structure
How you structure the variable component shapes what the role optimises for, and the default inherited from sales roles is wrong here.
- Tie variable to pipeline generated, not meetings booked. Meetings are the SDR's metric. A GTM engineer optimising for meeting volume will loosen scoring thresholds, which produces more meetings and less pipeline.
- Use a quarterly measurement window. The systems this role builds take six to ten weeks to show effect. Monthly variable pushes them toward short-horizon fixes.
- Keep variable at 10–20%. High variable suits roles with direct control over an outcome. This role has strong influence and partial control, and over-weighting variable makes it harder to hire well.
- Consider a systems-quality component. A portion tied to uptime and data-freshness SLAs on the pipelines they own. It is the only lever that reliably makes monitoring a priority before something breaks.
The first 90 days
Include this in the description. Candidates evaluating several offers consistently rank a specific 90-day plan above a modestly higher salary, and it forces you to be honest about the state of your stack.
| Window | Expected | Success looks like |
|---|---|---|
| Days 1–30 | Audit the current stack, data model, and enrichment spend | A written map of every automated job, its owner, and its failure mode |
| Days 31–60 | Ship one end-to-end play | A single signal wired through enrichment, scoring, routing, and outreach — with monitoring |
| Days 61–90 | Instrument and iterate | Signal-to-action latency measured and reduced; per-play cost per qualified opportunity reported |
One play shipped properly in 60 days beats five half-wired ones, and it establishes the measurement pattern everything afterwards inherits.
Where to find them
The supply is thin and mostly not looking. Three sources that work better than job boards: RevOps practitioners who have started building rather than reporting; analytics engineers with commercial curiosity, who bring the strongest data modelling in the pool; and senior SDRs or AEs who taught themselves automation because their quota depended on it — often the best commercial judgement, needing the most technical support in the first six months.
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