Go-to-Market Engineer

About this role

As our Go-to-Market Engineer, you'll be the person who actually builds and owns the systems that make our commercial data and workflows trustworthy, not just report on where they're broken. Right now, responsibility for Ably's commercial data and workflows is split across a few people and tools, each doing good work in their own area, but nobody's holding the full picture end to end. That's what we want this person to own.

This role sits at the intersection of Sales, CS, Marketing and Data Engineering. This isn't scoped to one function: it's scoped to wherever the commercial systems gap is, whether that's a marketing lifecycle problem today or a sales automation problem tomorrow. You might build an agent that flags an ICP account the moment its usage crosses a threshold and drafts the first outreach, or a nightly job that reconciles HubSpot and Snowflake data so nobody's arguing about whose number is right.

You'll spend most of your time putting infrastructure in place: workflows, data pipelines, AI-driven enrichment and automation. The rest goes into using that infrastructure to find what's actually wrong and fix it: data quality audits, funnel and conversion analysis, and diagnosing gaps in our commercial systems wherever they show up.

This isn't a dashboards-on-request role. You won't be handed tickets and told what to build. We want someone who thinks like an owner: you see the gap before anyone else notices it, decide what's worth building, and go build it, not because something's on fire, but because you can see a better system than the one that exists today.

Day to day, you will

• Build and own the systems, not just the outputs. The majority (~70%) of this role is building: automated workflows, data pipelines, lead scoring and routing logic, and AI-driven enrichment across our CRM, sales and marketing stack. You choose the stack and approach; we care about reliability, not which tool you picked. The other ~30% is using what you've built to run data quality audits, diagnose conversion issues and analyse performance.
• Automate where possible and fix broken or manual workflows wherever you find them. That might be Sales flagging something that isn't automated yet (prospecting, outreach, data hygiene), poor delivery, bounce or open rates on a nurture sequence, or a sales process still being done by hand. This role isn't scoped to marketing specifically, it's scoped to wherever the gap is.
• Share HubSpot's day-to-day upkeep, including Marketing Hub, as part of the team's rotation. This isn't an admin role, the job is building systems on top of HubSpot data, but routine maintenance is shared across the team.
• Run data quality audits. Validate enrichment accuracy across our vendors, spot-check that account and lead classification is actually correct, and dig into why a number's moved rather than shrugging it off.
• Own conversion and funnel tracking. Map where prospects drop off across our automated systems, from first touch through to deal, and turn that into something the business can actually act on.
• Be the connective tissue between commercial and data teams. Take requirements from stakeholders, translate them into a working system, and push back when a request is really a symptom of something else that needs fixing at the root.
• Use AI to operate at a scale one person shouldn't otherwise manage. You're comfortable building an AI-assisted enrichment or automation workflow largely unsupervised, and you instinctively reach for AI tooling to keep what you build self-sustaining rather than manually maintained.

We'd love to talk if you have

• Experience in Marketing Ops, Sales Ops, Revenue Ops or a similar cross-functional systems role, ideally with AI-driven or automated workflows already under your belt.
• Comfort working across CRM platforms in general. We use HubSpot, but this role exists to build systems using HubSpot data, not to become its day-to-day maintainer. If you've worked deeply in Salesforce or another CRM, that transfers fine.
• A track record of building things independently: given a data or workflow problem, you can pick an appropriate stack and build a working solution without being told how.
• Hands-on experience with workflow automation tools (n8n, Zapier, Make or similar). This is a must, not a nice-to-have. You should be comfortable designing and shipping a multi-step automated workflow yourself, not just configuring templates.
• You've built with LLMs beyond basic prompting: agentic workflows, custom AI tooling, MCP connections or similar. This is also a must, we want evidence you've built something an agent runs, not just used a chatbot for research.
• A systems-thinking mindset and genuine analytical ability. You can look at delivery, bounce or open-rate data, or a lifecycle funnel, and diagnose why it looks the way it does, then fix the process that caused it rather than patch the symptom in front of you.
• Comfort with ambiguity. You've got strong opinions and can hold your own view on what's worth fixing first, even when priorities aren't obvious or agreed.

Bonus points if you have

• Familiarity with the Claude stack specifically (Claude Code, Claude API, MCP). This is what we use internally, so prior exposure is preferred though not required.
• Experience with lead lifecycle/funnel models (MQL, SAL, PQL, SQL) and how scoring and routing logic works in practice.