ATQLeads builds go-to-market systems for B2B companies that have stopped relying on agencies and can't justify full-time hires. We occupy the space between: a network of vetted GTM operators who plug directly into client teams — their tools, their Slack, their stack — and deliver results, not billable hours.
We don't sell retainers. We deploy capacity. Operators are part of the company. If you want to own meaningful work, operate inside real businesses, and be paid like a partner rather than a vendor, this is the right place.
Role Overview
The Growth Marketer is the experimentation engine that turns a functional funnel into one that compounds over time.
Without a Growth Marketer, a client stays reactive. Funnel problems get fixed by guessing instead of testing. Retention goes unmeasured. Growth loops stay on the whiteboard. Acquisition spend scales linearly instead of compounding. The Growth Marketer finds where the funnel is losing users, designs the experiment that addresses it, and verifies whether it actually worked.
You won't receive campaign assignments. You'll be handed a funnel, a product, and a capacity commitment. Your job is to pinpoint the highest-impact bottleneck, design and run the experiment that targets it, and turn the learnings into a system that builds on itself.
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 vs. variant, sample size estimate, runtime, and decision rule (which outcome drives which action). Multi-variate and sequential test programs when multiple variables need testing. An experimentation roadmap that sequences tests so each result feeds the next, generating compounding learnings across a quarter or more. Prioritization by expected value: estimated impact × confidence × ease of execution. Success criteria defined before launch, not after results come in.
Funnel Diagnostics and Optimization — funnel analyses showing conversion rates at every stage (signup → activation → paid conversion → retention). Cohort retention curves that show when and where users drop off. 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 surface the stage with the largest performance gap. Root cause diagnosis: whether a funnel problem stems from targeting (wrong users entering), messaging (right users, wrong framing), product experience (users enter but don't activate), or pricing (users activate but don't 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 tied to actual product behavior, not generic "invite a friend" prompts. Understanding of how retention multiplies every acquisition investment — a 10% retention improvement compounds the value of all existing and future acquisition spend. Mapping all growth mechanisms for a business, identifying the loop with the highest expected return, and determining what it takes 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 gone silent. Conversion acceleration triggers: specific user actions or time-based conditions that initiate a targeted campaign to push users forward.
Three Phases of Execution
Your work runs in three modes depending on where the engagement stands:
Foundation — set up experiment tracking infrastructure so every test produces interpretable data. Map the full user journey from first contact to paying customer to advocate, capturing 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 miss. 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 show up in lagging metrics (e.g., declining feature usage that precedes 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 enters the funnel for experiments to yield valid results. Deliverable: continuous experimentation generating documented learnings and measurable funnel improvements.
Optimization — reduce CAC and improve LTV over time through tighter targeting and funnel optimization, measured against the baselines set in the foundation phase. Build and scale validated growth loops from manual to automated execution. Understand and manage the relationship between paid acquisition efficiency and organic growth loops — when paid spend subsidizes organic growth, and when it works against it. Deliverable: compounding growth systems with improving unit economics.
Cross-Functional Collaboration
Your work depends on inputs from three roles: 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 degrade the quality of your experiments.
You coordinate with the GTM Engineer to ensure experiments are built correctly and tracking is live. You coordinate with the Digital Marketing Specialist to ensure enough traffic is flowing into the funnel for results to be valid. You receive product quality, offer strength, and sales feedback from the client, and flag when any of these are insufficient for experiments to produce actionable data.
You share experiment results and growth insights with the client and execution team on a regular cadence. Every recommendation is grounded 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 to be handed a test. 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 when to let it run (insufficient sample or ambiguous result). You understand and account for novelty effects (short-term spikes that don't persist), regression to the mean, and test pollution (one experiment contaminating another's results).
You document each experiment's result, the decision it drove, and the next test it triggered, so the team can follow the reasoning chain. You build systems that compound — each experiment informs the next, producing a learning engine that gets sharper over time.
You do not own channel-level execution: running paid ads, writing blog posts, or managing social media — that belongs to the Digital Marketing Specialist. You do not own GTM strategy: ICP definition, positioning, messaging frameworks, or channel prioritization — that belongs to the GTM Strategist. You do not own technical system implementation: building automations, configuring CRM workflows, or setting up data pipelines — that belongs to the GTM Engineer.
You flag when a client has reached the ceiling of their current experimentation bandwidth — wanting to test more ideas, improve multiple funnel stages at once, 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 capable 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 pull data for analysis without depending 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 pick up 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 an onboarding flow experiment"), not activity terms ("ran 12 experiments this quarter")
- You understand 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 needing to be 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 outcomes, and adjust based on what actually happened
- You don't 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 availability and skill fit, working fractionally inside one or more client teams at a time.
Once 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 compensated 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 the highest-retention users, not just the highest volume
- Copywriting — enough to write experiment variants (landing pages, emails, onboarding flows) without waiting on a content team
Pay: $27.00-$51.00 per hour
Work Location: Remote
