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Ably is a realtime infrastructure platform powering billions of messages daily for organizations like Harness, EA, Panasonic, and HubSpot. This Go-to-Market Engineer role owns the end-to-end commercial data and workflow systems that currently sit fragmented across Sales, Customer Success, Marketing, and Data Engineering.
You'll spend approximately 70% of your time building infrastructure: automated workflows, data pipelines, lead scoring and routing logic, and AI-driven enrichment across the CRM, sales, and marketing stack. The remaining 30% involves running data quality audits, diagnosing conversion issues, analyzing funnel performance, and identifying gaps in commercial systems.
Key responsibilities include:
- Building and owning reliable systems for commercial workflows and data pipelines, not just reporting on breakdowns
- Automating manual processes across Sales, Marketing, and CS functions (prospecting, outreach, data hygiene, nurture sequences)
- Sharing HubSpot day-to-day maintenance as part of team rotation, with focus on building systems on top of HubSpot data
- Running data quality audits to validate enrichment accuracy and spot-check account/lead classification
- Owning conversion and funnel tracking from first touch through deal closure
- Acting as connective tissue between commercial and data teams, translating requirements into working systems
- Leveraging AI to build self-sustaining, largely unsupervised enrichment and automation workflows
This role is scoped to wherever commercial systems gaps exist—not confined to marketing or sales alone. You'll think like an owner, identifying gaps before they're noticed and deciding what's worth building based on systems thinking, not just reactive firefighting.
Requirements:
- Experience in Marketing Ops, Sales Ops, Revenue Ops, or similar cross-functional systems role, ideally with AI-driven or automated workflows
- Comfort working across CRM platforms (HubSpot experience preferred, but Salesforce or other CRM experience transfers well)
- Track record of building things independently: given a data or workflow problem, you can pick an appropriate stack and ship a solution without direction
- Hands-on experience with workflow automation tools (n8n, Zapier, Make, or similar)—this is a must-have. You should design and ship multi-step automated workflows yourself, not just configure templates
- Demonstrated experience building with LLMs beyond basic prompting: agentic workflows, custom AI tooling, MCP connections, or similar. This is also a must-have; evidence of building something an agent runs, not just using a chatbot for research
- Systems-thinking mindset and genuine analytical ability. You can diagnose why delivery, bounce, open-rate data, or lifecycle funnels look the way they do, then fix the root process rather than patch symptoms
- Comfort with ambiguity and strong opinions on what's worth fixing first, even when priorities aren't obvious or agreed
Bonus:
- Familiarity with Claude stack (Claude Code, Claude API, MCP)
- Experience with lead lifecycle/funnel models (MQL, SAL, PQL, SQL) and scoring/routing logic in practice
