GTM Engineer

HiveMQ

GTM Engineer responsible for constructing lead qualification infrastructure and deploying AI solutions for HiveMQ's product-led growth initiatives. Engage in multiple operational bottlenecks across GTM motions.

Posted 5/13/2026full-timeRemote • 🇺🇸 United StatesJuniorMid-LevelWebsite

Tech Stack

Tools & technologies

AWSJavaScriptPython

About the role

Key responsibilities & impact

Own the end-to-end inbound and PLG funnel infrastructure - from first product signal or form fill to the right next step - ensuring every lead is enriched, scored, and routed into the right GTM motion without manual triage.
Build automated paths for transactional and mid-market demand that don't require AE time: self-serve, low-touch sequences, and AI-assisted qualification that moves leads toward close independently.
Deploy agentic AI systems across marketing functions - research, lead qualification, personalized follow-up, meeting booking.
Design and operate lead qualification models that route leads to the right GTM motion based on first- and third-party signals, product usage, firmographics, behavioral data, and more.
Build pre-AE qualification and nurture infrastructure so leads that reach a rep are warm, educated, and ready - not raw inbound that eats selling time.
Build and maintain the orchestration layer across HubSpot, Salesforce, Clay, Default, Slack, G-suite, and more that connects demand to revenue at every segment.
Build agentic AI support layer across marketing and sales functions.
Deploy AI agents that do the research, qualification, and follow-up work that doesn't scale manually.
Define what the transactional path to close looks like at HiveMQ when an AE isn't the right answer.

Requirements

What you’ll need

2–4 years of experience in GTM engineering, marketing operations, or revenue operations - with a track record of building systems, not just configuring them.
Hands-on experience with HubSpot and Salesforce as a CRM system of record.
Practical fluency with enrichment and automation tooling - Clay, Clearbit, Default, Hubspot, or comparable platforms.
Experience building or deploying AI agents or LLM-powered workflows (Claude Code, Codex, etc.) in a GTM or revenue context - not just prototypes, but trusted systems that run in production at scale.
Comfort with APIs, webhooks, and light scripting (Python or JavaScript) to build around vendor limitations when off-the-shelf output isn't good enough.
Comfort with large data sets used to inform GTM engineering and workflows in AWS, GBQ, etc.
Experience building lead qualification and routing logic - scoring, segment-based routing, SLA definition and enforcement.
A systems mindset: you think about edge cases, failure modes, and what happens at 10x volume before you ship.
The autonomy to move from concept to production in a remote environment without constant oversight.

Benefits

Comp & perks

No manual processes with autonomous workflows.
Build enrichment pipelines that surface firmographic, technographic, and product context before the first human touchpoint - no manual research.
Partner with Marketing, Demand Gen, Sales, and GTM Ops to translate operational bottlenecks into engineered, automated solutions.

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

GTM engineeringmarketing operationsrevenue operationslead qualification modelsAI agentslight scriptingPythonJavaScriptdata analysisautomation tooling

Soft Skills

systems mindsetautonomyproblem-solvingcritical thinkingattention to detailcommunicationcollaborationadaptabilitytime managementstrategic thinking