GTM Engineer

• Build Product Qualified Lead (PQL) identification systems that surface enterprise buying signals based on team expansion, engagement, feature adoption, and company attributes
• Build AI agents for automated account research using LLM APIs to analyze company websites, news, funding events, and tech stacks, generating personalized talking points
• Design and implement data pipelines from product usage data to HubSpot, enabling sales and CS teams to see real-time engagement, usage trends, and expansion signals
• Create AI-powered lead scoring models using machine learning algorithms combined with product behavior, firmographics, and engagement patterns to predict conversion
• Build dashboards and reporting that give sales, CS, and leadership visibility into account health, product adoption, expansion opportunities, and churn risk
• Implement reverse ETL infrastructure using tools like Census, Hightouch, or custom solutions to ensure product data flows seamlessly into GTM systems

• 3–5 years of experience in a GTM Engineer, Growth Engineer, Revenue Ops, or Analytics Engineering role at a PLG B2B SaaS company
• Strong technical foundation in Python and SQL with experience building data pipelines, ETL/reverse ETL workflows, and integrating product data with GTM systems
• API integration experience and expertise with workflow automation tools like n8n, Zapier, Make, or Tray.io for building API-driven workflows
• Deep understanding of PLG metrics and CRM systems with ability to operationalize activation, engagement, and expansion signals in HubSpot or Salesforce
• Proven impact and business acumen with track record of building systems (PQL models, AI agents, predictive analytics) that drove measurable pipeline or revenue
• Scrappy builder mindset with ability to balance custom builds vs. off-the-shelf tools and iterate based on feedback

• Data warehouse experience (Snowflake, BigQuery, Redshift) and familiarity with dbt or similar transformation tools
• Previous experience helping build the early data systems fueling the transition from PLG to enterprise (Figma, Notion, Slack, Miro, Airtable)
• Production machine learning experience building, deploying, and monitoring predictive models

competitive equity