This is a remote position.

ATQLeads builds GTM systems for B2B companies that are done waiting on agencies and tired of the cost of full-time hires. We sit between the two: a network of vetted GTM operators who embed directly inside client teams — inside their tools, their Slack, their stack — and deliver against outcomes, not hours.

We don't sell retainers. We deploy capacity. Operators are part of the company. If you want to own real work, operate inside real companies, and be compensated like a partner rather than a contractor, this is where you belong.

Role Overview

The GTM Engineer is the engine builder at the core of every ATQ Squad engagement.

Strategy without a GTME stays theoretical. Campaigns stay manual. Data stays fragmented. Pipeline stays unpredictable. The GTME is the person who turns direction into operational machinery and then keeps that machinery running.

You won't be assigned tickets. You'll be given a GTM problem, a client stack, and a capacity commitment. Your job is to build the system that solves it, own its reliability, and push it forward without being asked.

What You'll Do

Technical and System Building

You own four interconnected systems end-to-end:

Outbound, Inbound and Pipeline Generation Engine — multi-channel sequences across email, LinkedIn, phone, and video, with sequence logic appropriate to each channel. Sending infrastructure and deliverability management: domain warmup, sender rotation, content variation, blacklist monitoring. Inbound capture via webhooks from forms, webinars, intent feeds, and website behavior. Product-led activation flows (signup → first key product action → conversion trigger) when product event data is available.

Data and Enrichment System — lead sourcing, TAM mapping, waterfall enrichment across multiple data sources, segmentation, and signal-based targeting using firmographic, technographic, hiring, funding, intent, and behavioral signals. Custom signals built from any data source, not only pre-built intent feeds. Composite scoring models that combine multiple signals. Signal expiration and refresh cadence management per source.

Automation and Workflow Layer — end-to-end workflows across Clay (multi-source tables, formula columns, Claygent prompts), Make/n8n (multi-step logic, error handling, retries, webhooks), Apify, and CRM. API and webhook integrations as the default approach, including when native integrations exist. LLM API calls (Claygent, OpenAI, Anthropic) integrated into production workflows for enrichment, classification, or copy generation. Reusable modules (Clay Functions, Make sub-scenarios) so logic is not duplicated across tables. Python and pandas for data work no-code tools cannot handle.

Conversion Infrastructure — reply handling logic, meeting routing and scheduling flows, CRM hygiene (HubSpot, Zoho, or Salesforce), and tracking and attribution infrastructure built in at system setup, not added afterward. Dashboards that show conversion rates and volume at each funnel step. Signal source mapped to conversion outcome, so you can identify the step with the largest performance gap and fix it there.

Three Phases of Execution

Your work runs in three modes depending on where the engagement is:

Foundation — build the initial outbound and data infrastructure, translate the GTM Strategist's ICP into Clay filter logic, configure CRM workflows, and establish the reporting layer. Define and instrument the metrics needed to evaluate system performance before launch. Deliverable: one functional GTM engine ready to generate pipeline, with monitoring at each step and documentation alongside the build — handoff doc, README, Loom walkthrough.

Ongoing — launch and manage campaigns, enrich and refresh lead lists, monitor deliverability, handle replies, and maintain CRM accuracy (deals progress through stages, are moved, deduplicated, and closed when stale). Calculate conversion rates at each funnel step (open, reply, positive reply, meeting set, show). Deliverable: consistent campaign execution and pipeline flow.

Optimization — A/B test messaging, targeting logic, and conversion paths. Prioritize improvements at the funnel step with the largest gap between current performance and benchmark, not by stakeholder requests. Expand into new segments and channels once the existing system delivers consistently. Reduce manual steps systematically by automating any step still requiring human action. Refine ICP using actual response and conversion data, not only the initial hypothesis. Deliverable: improving efficiency and scaling pipeline generation.

Cross-Functional Collaboration

You depend on inputs to do your job well: ICP definition and positioning from the GTM Strategist, offer clarity and CRM access from the client, and clean data from your enrichment sources. Weak inputs reduce output efficiency, so you'll need to be direct about what you need and when. Manage upstream dependencies actively: report data quality issues, flag missing Strategist inputs, and request clarification when a brief lacks the inputs needed for accurate delivery.

You'll work closely with sales, marketing, and growth ops teams as a peer. No jargon. No vendor distance. You're inside the operation. You communicate directly with clients without a project manager intermediary.

Strategic Ownership

After onboarding, you generate your own ideas. You don't wait for a brief. You diagnose bottlenecks early — identify which step of the system is causing performance issues (copy, list quality, deliverability, routing, or timing) and resolve at that step. You prioritize by revenue impact and build experiments that compound. You think like an operator: what's the highest-leverage thing to ship this week?

You build systems that continue functioning after you leave the engagement, with no required maintenance from the original builder. A track record of at least one shipped system still running at a previous engagement after you left is the bar.

You'll Thrive Here If You...

1. Have real technical range

You don't need to be full-stack, but you need to be sharp with:

• Python, JavaScript, or SQL — including pandas for cleaning, deduplicating, and transforming CSV/Excel data
  
• APIs, webhooks, and structured data — used as the default approach, including when native integrations exist
  
• Clay (required) — multi-source tables, waterfall enrichment, formula columns, Claygent prompts
  
• Make (preferred) or n8n — multi-step scenarios with error handling, retries, and webhooks
  
• Zapier, Retool, Apify, and equivalent web scraping tools (BeautifulSoup, Playwright) when standard enrichment sources do not return the required data
  
• LLM API integration (Claygent, OpenAI, Anthropic) into production workflows, with prompts that produce consistent and accurate output
  
• CRM configuration (HubSpot, Zoho, or Salesforce) — workflows, custom objects, lead routing, lead scoring, bidirectional sync
  
• Debugging fast and using AI as a co-pilot, not a crutch
  

You learn new tools on your own. You figure things out before asking.

2. Think in outcomes, not tasks

• You ask "why" before building something
  
• You know what a qualified lead costs, why conversion rates matter, and how your work connects to a number
  
• You describe your work in outcome terms ("produced X meetings via Y% conversion"), not activity terms ("sent X emails")
  
• You can explain a multi-step workflow in 60 seconds
  
• You communicate in plain language to people who care about results, not your stack
  
• When a technical decision is challenged, you revise it or add reasoning — you don't restate the original answer
  
• You acknowledge gaps in your knowledge and propose how to resolve them
  
• You manage your own priorities without being managed
  

3. Operate like you own it

• You can spot a broken funnel before anyone tells you
  
• You improve a process before you automate it
  
• You treat client systems like your own, with urgency, ownership, and judgment
  
• You deliver a working artifact within 72 hours of an unstructured brief
  
• Documentation ships with every deliverable — handoff docs, READMEs, Loom walkthroughs are part of the build, not an afterthought
  
• You plan for failure modes (enrichment fallback, deduplication, sync failures, deliverability blacklist) before they break the system
  
• The best signal we look for: operators who ship something valuable in week one, without being asked
  

How It Works

This is not a full-time role. You'll be matched to client engagements based on your availability and skill fit, working fractionally inside one or more client teams at a time.

Once you're vetted and onboarded into the ATQ network, deployment is the next step. From day one, you're embedded inside the client's tools: Slack, HubSpot, Notion, Salesforce. You operate as part of their team, not as an outside vendor.

Work is structured around Capacity Units (CUs) — defined outputs with clear scope, not open-ended time commitments. You are paid for what you ship.

With ATQ, you are paid fairly and your reputation travels with us across every project.

Requirements

Hard requirements (must have)

• Clay — multi-source tables, waterfall enrichment, formula columns, Claygent prompts
  
• Make or n8n — multi-step scenarios with error handling, retries, webhooks
  
• Python, JavaScript, or SQL (at least one)
  
• APIs and webhooks as the default integration approach
  
• LLM API integration (Claygent, OpenAI, or Anthropic) into production workflows
  
• CRM configuration on HubSpot, Zoho, or Salesforce — workflows, custom objects, routing, scoring, bidirectional sync
  
• Track record: at least one shipped system still running at a previous engagement after you left
  
• Fluent in English
  

Working knowledge expected

• Apify or equivalent web scraping (BeautifulSoup, Playwright) when standard enrichment fails
  
• Zapier, Retool
  
• Deliverability ops: domain warmup, sender rotation, content variation, blacklist monitoring
  
• Composite scoring across firmographic, technographic, hiring, funding, intent, behavioral signals
  
• Failure-mode planning: enrichment fallback, deduplication, sync failures, deliverability blacklist
  
• pandas for CSV/Excel cleaning and transformation
  

Operating requirements

• Async-first work in Slack, Notion, Linear
  
• Deliver a working artifact within 72 hours of an unstructured brief
  
• Ship documentation with every build (handoff doc, README, Loom)
  
• Communicate directly with clients — no PM intermediary
  
• Manage your own priorities and blockers
  
• Report upstream data and strategy quality issues, don't work around them
  

Benefits

• Fully remote work environment
  
• Freedom to work whenever you want
  

If you want to do the best work of your career while shaping the next generation of AI-powered go-to-market systems, this role is for you.