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 Growth Marketer is the experimentation engine that turns a working funnel into a compounding one.
A client without a Growth Marketer stays reactive. Funnel problems get patched by guessing instead of testing. Retention stays unmeasured. Growth loops stay theoretical. Acquisition spend scales linearly instead of compounding. The Growth Marketer is the person who finds where the funnel is losing users, designs the experiment that fixes it, and measures whether it actually worked.
You won't be assigned campaigns to run. You'll be given a funnel, a product, and a capacity commitment. Your job is to identify the highest-impact bottleneck, design and run the experiment that addresses it, and build the learnings into a system that compounds over time.
What You'll Do
Technical and System Building
You own four interconnected systems end-to-end:
Experimentation Engine — structured experiments for each test cycle, each with a defined hypothesis, target metric, control group vs. variant, sample size estimate, runtime, and decision rule (what outcome triggers which action). Multi-variate and sequential test programs when a situation requires testing multiple variables. An experimentation roadmap that sequences tests so each result informs the next, producing compounding learnings across a quarter or longer. Prioritization by expected value: estimated impact multiplied by confidence multiplied by ease of execution. Success criteria defined before launch, not after results arrive.
Funnel Diagnostics and Optimization — funnel analyses showing conversion rates between each stage (signup → activation → paid conversion → retention). Cohort retention curves that identify when and where users stop returning. Segmented cohort analysis comparing retention or conversion across user segments (acquisition source, plan type, geography, behavior pattern). Full AARRR (Acquisition, Activation, Revenue, Retention, Referral) audits that identify the stage with the largest performance gap. Root cause diagnosis: whether a funnel problem is caused by targeting (wrong users entering), messaging (right users, wrong framing), product experience (users enter but do not activate), or pricing (users activate but do not convert). Prioritized fix lists ranked by expected impact and effort.
Growth Loops and Compounding Systems — identification of which growth loops apply to a given business: referral loops, content flywheels, product-led loops, lifecycle loops, or community-driven loops. Referral mechanics and expansion triggers connected to actual product behavior, not generic "invite a friend" prompts. Understanding of how retention multiplies every acquisition investment — a 10% improvement in retention compounds the value of all existing and future acquisition spend. Mapping all growth mechanisms for a given business, identifying which loop has the highest expected return, and determining what is required to make it work. Scaling validated loops from manual to automated execution.
Lifecycle and Nurturing Systems — lead nurturing flows (email sequences, retargeting, in-app messages) that move users from one funnel stage to the next. Re-engagement campaigns for users who have stopped using the product or stopped responding to outreach. Conversion acceleration triggers: specific user actions or time-based conditions that initiate a targeted campaign to move the user forward.
Three Phases of Execution
Your work runs in three modes depending on where the engagement is:
Foundation — set up experiment tracking infrastructure so that every test produces interpretable data. Map the full user journey from first contact to paying customer to advocate, identifying each transition point and its current conversion rate. Configure custom events in a product analytics tool (Mixpanel, Amplitude, or equivalent) to track user behaviors that default reports do not capture. Establish baseline CAC (Customer Acquisition Cost), LTV (Lifetime Value), and the ratio between them, segmented by acquisition channel and user cohort. Deliverable: a working analytics and tracking layer, a baseline funnel map with conversion rates at each stage, and a prioritized experimentation roadmap.
Ongoing — run experiments against the highest-impact funnel bottleneck. Analyze results: what changed, what was learned, what the team should do differently. Identify which acquisition channels produce users with the highest retention and LTV, not just the highest volume. Identify leading indicators of churn or expansion before they appear in lagging metrics (e.g., drop in feature usage preceding cancellation). Coordinate with the GTM Engineer to ensure experiments are implemented correctly and tracking is in place. Coordinate with the Digital Marketing Specialist to ensure sufficient traffic volume enters the funnel for experiments to produce valid results. Deliverable: continuous experimentation producing documented learnings and measurable funnel improvements.
Optimization — reduce CAC and improve LTV over time through more precise targeting and funnel optimization, measured against the baselines established in foundation. Build and scale validated growth loops from manual to automated execution. Understand and manage the interaction between paid acquisition efficiency and organic growth loops — when paid spend subsidizes organic growth, and when it cannibalizes it. Deliverable: compounding growth systems with improving unit economics.
Cross-Functional Collaboration
You depend on inputs from three roles to do your job well: positioning and ICP direction from the GTM Strategist, technical implementation of automations and data pipelines from the GTM Engineer, and channel-level traffic from the Digital Marketing Specialist. Weak inputs at any of these points reduce the quality of your experiments.
You coordinate with the GTM Engineer to ensure experiments are implemented correctly and tracking is in place. You coordinate with the Digital Marketing Specialist to ensure sufficient traffic volume enters the funnel for experiments to produce valid results. You receive product quality, offer strength, and sales feedback from the client and flag when any of these are insufficient for experiments to produce valid results.
You communicate experiment results and growth insights to the client and execution team on a regular cadence. You anchor every recommendation in data: the metric, the observed value, the expected value, and the proposed action.
Strategic Ownership
After onboarding, you generate your own experiment ideas. You don't wait for someone to hand you a test to run. You identify the specific funnel step where users are dropping off, form a testable hypothesis about why, and design the experiment that addresses it. You use data to decide what to test next, not only to report on what already happened.
You know when to call a test early (clear signal with sufficient data) versus let it run to completion (insufficient sample or ambiguous result). You understand and account for novelty effects (short-term spikes that do not persist), regression to the mean, and test pollution (one experiment contaminating another's results).
You document each experiment's result, the decision made from it, and the next test it triggered, so the team can trace the reasoning chain. You build systems that compound — each experiment informs the next, producing a learning engine that gets more effective over time.
You do not own channel-level execution: running paid ads, writing blog posts, or managing social media — that is the Digital Marketing Specialist's scope. You do not own GTM strategy: ICP definition, positioning, messaging frameworks, or channel prioritization — that is the GTM Strategist's scope. You do not own technical system implementation: building automations, configuring CRM workflows, or setting up data pipelines — that is the GTM Engineer's scope.
You signal when a client has reached the limits of their current experimentation bandwidth — wanting to test more ideas, improve multiple funnel stages simultaneously, or scale faster — and recommend a tier upgrade.
You'll Thrive Here If You...
1. Have real technical range
You don't need to be a data scientist, but you need to be sharp with:
- Product analytics tools (Mixpanel, Intercom, or equivalent) — configure custom events, build funnels, run cohort analyses, identify leading indicators
- SQL — enough to query databases and extract data for analysis without waiting on a data team
- CRM (HubSpot, Salesforce) — understand pipeline stages, track attribution, segment by cohort
- Experiment design — A/B tests, multi-variate tests, sequential test programs, sample size estimation, statistical significance
- Copywriting — enough to write experiment variants (landing pages, emails, onboarding flows) without waiting on a content team
- Spreadsheet modeling — CAC/LTV analysis, cohort tables, experiment tracking, prioritization matrices
You learn new tools on your own. You figure things out before asking.
2. Think in outcomes, not tasks
- You ask "what metric will this move?" before designing an experiment
- You describe your work in outcome terms ("improved activation rate by X% through onboarding flow experiment"), not activity terms ("ran 12 experiments this quarter")
- You know what CAC, LTV, and the ratio between them mean for a business, and you can explain how your work affects each
- You can explain how retention compounds acquisition investment in 60 seconds
- You communicate in plain language to people who care about growth, not your methodology
- When an experiment result is ambiguous, you extend the test or redesign it — you don't cherry-pick the interpretation
- You acknowledge gaps in your data and flag when traffic volume is insufficient for valid results
- You manage your own experiment roadmap without being managed
3. Operate like you own it
- You can spot a funnel bottleneck before anyone tells you
- You stop underperforming experiments based on data, not sunk cost
- You treat client funnels like your own, with urgency, ownership, and judgment
- You deliver a baseline funnel map and prioritized experiment roadmap within the foundation phase
- Every experiment ships with defined success criteria, documented results, and the next test it triggered
- You flag when product quality, offer strength, or traffic volume are insufficient for experiments to produce valid results — you don't run tests you know will be inconclusive
- You stay long enough to observe whether experiments produced the expected results, and adjust based on what actually happened
- You do not confuse correlation with causation when reading experiment results
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
Required Software:
- Product analytics — Mixpanel, Amplitude, or equivalent (configure custom events, build funnels, cohort analyses, retention curves)
- SQL — query databases to extract data for analysis
- CRM — HubSpot or Salesforce (pipeline tracking, attribution, cohort segmentation)
- Slack, Notion — embedded client communication and documentation
- Spreadsheets — CAC/LTV modeling, cohort tables, experiment tracking, prioritization
Required Skills:
- Experimentation design — structured experiments with hypothesis, target metric, control/variant, sample size, runtime, decision rules. Multi-variate and sequential tests. Prioritize by impact × confidence × ease. Know when to call a test early vs. let it run. Account for novelty effects, regression to the mean, and test pollution
- Product analytics and funnel diagnostics — build and interpret funnel analyses (signup → activation → paid → retention), cohort retention curves, segmented cohort analysis by acquisition source/plan/geography/behavior. Identify leading indicators of churn or expansion before lagging metrics show it
- Growth loops — identify which loops apply (referral, content flywheel, product-led, lifecycle, community). Design referral mechanics tied to actual product behavior. Understand how retention compounds acquisition investment. Scale validated loops from manual to automated
- Funnel optimization (AARRR) — audit all five stages, find the largest gap, diagnose root cause (targeting, messaging, product experience, or pricing), prioritize fixes by impact and effort
- Lifecycle and nurturing — design nurture flows (email, retargeting, in-app), re-engagement campaigns, conversion acceleration triggers
- Tracking and attribution — CAC, LTV, CAC:LTV ratio segmented by channel and cohort. Identify which channels produce highest-retention users, not just highest volume
- Copywriting — enough to write experiment variants (landing pages, emails, onboarding flows) without waiting on a content team
Pay: $19.00-$36.00 per hour
Expected hours: 10 – 40 per week
Work Location: Remote
