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HiveMQ is the Industrial AI Platform helping enterprises move from connected devices to intelligent operations.
GTM Engineer
Location
United States
Posted
68 days ago
Salary
0
Seniority
Mid Level
Job Description
GTM Engineer
HiveMQ
• 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.
Job Requirements
- 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
- 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.
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