GTM Engineer

Join OpenGov as a Forward Deployed Engineer, where you'll be at the forefront of transforming the revenue organization through applied AI. In this technical role, you'll design and build workflows, integrations, and automations that enhance seller efficiency. You'll work closely with sales leadership, RevOps, and various sales teams to understand their needs and drive adoption of the tools you create.

This is an opportunity to make a significant impact in a mission-driven company. - Concevoir, construire et améliorer continuellement les agents et les automatisations qui transforment les signaux de marché en actions qualifiées pour les SDR, les AE et les CSM. - Agir en tant que propriétaire de produit pour les systèmes entièrement développés en interne, en pilotant la feuille de route, la priorisation et la qualité des systèmes de renseignement sur les appels et les affaires. - Collaborer avec les équipes de vente, de RevOps et d'analytique pour comprendre les processus actuels et identifier les opportunités d'amélioration grâce à l'IA. - Demonstrated experience using AI tools in a professional context — not just familiarity.

You have been actively using tools like Claude, Cursor, or Codex, as well as agentic orchestration systems like n8n, , or Airia, to meaningfully improve how you or your team works, and you can speak concretely about what you've built or changed as a result - Past experience/exposure to GTM and Sales at a SaaS company - Strong user research and discovery skills: you build trust with sellers and RevOps before designing for them, surface the root problem underneath the surface problem, and validate solutions with the people who will actually use them - 4–7 years of experience in technical product management, sales/revenue operations, data science, GTM strategy or operations, solutions architecture, or a closely adjacent function.

Backgrounds vary: a technical PM, a data scientist, a sales ops analyst who builds, or a GTM strategist who has taught themselves to build with Claude are all strong fits. Deep software engineering backgrounds are welcome when paired with sales or revenue operations experience.

We need someone who has been hands-on, on the ground, building and improving things — not advising from a distance - Ability to bridge technical and business stakeholders: you can explain a complex AI workflow to a sales stakeholder, articulate data access tradeoffs to a compliance stakeholder, and translate business requirements into engineering specs — all in the same week or even day - Strong analytical skills: comfortable with data in spreadsheets, dashboards, or basic BI tools (Tableau, Looker, or similar), and working fluency with SQL; able to extract insight from data and frame it clearly for both technical and non-technical audiences - Exceptional written and verbal communication: you write clearly, you do not create information silos, and you know how to tailor your message to the audience in front of you - Exceptional problem-solving and first-principles thinking: you can look at a messy, manual seller process, pull it apart into its components, and design a fundamentally better solution — not just automate what exists today.

You approach unfamiliar domains as an asset, not a liability - Self-starter with high initiative: you see opportunities, propose solutions, and drive them forward without waiting to be told.

This role will involve significant ambiguity, and the right person will find that energizing rather than paralyzing - Production experience building and operating agents, including tool use, context engineering, and evaluation frameworks — you can point to something you shipped and describe how you knew it was working - Bachelor's degree in Information Systems, Computer Science, Business Analytics, Data Analytics, or a related field - Comfort working in a code-first approach to traditionally no-code enterprise sales systems (Salesforce, Outreach, Gong, or similar).

Familiarity with how enterprise revenue data flows across systems is a plus - Prior experience in sales/revenue operations, customer success operations, or a forward-deployed/solutions-architecture role - Familiarity with AI governance concepts: prompt design, data access controls, audit trails, and responsible AI principles in sensitive-data environments - Experience with Claude Code, the Claude Agent SDK, or building MCP servers - Experience building or deploying workflow automations or internal tools without engineering support - Familiarity with Snowflake, Looker, or similar data platforms at a data analysis level (not data engineering) - Applied ML and experimentation background:

A/B testing, propensity models, recommendations, or lead/account scoring - Experience working in a fast-moving, high-growth SaaS environment where the org is actively evolving and priorities shift quickly - Experience working at the intersection of Sales and Marketing, or Sales and RevOps — roles that required navigating cross-functional data ownership and system permissions - Prior exposure to public sector or B2G sales, or other environments with elevated data handling standards