GTM Engineer

Gamma•San Francisco, CA

1d•$170,000 - $215,000•Onsite

About The Position

You'll build the AI-native GTM systems and data infrastructure that turn product usage signals into enterprise sales opportunities. Gamma's PLG flywheel generates enormous engagement data across millions of users. Your job is to create the systems that identify which accounts should talk to sales, when they're ready, and why. This is a 0-to-1 role at the intersection of data, product, and revenue. You'll build Product Qualified Lead identification systems, design AI-powered lead scoring models, and implement data pipelines that give sales and customer success real-time visibility into engagement and expansion signals. You'll partner with Product and Data teams to instrument tracking, ensure data quality, and continuously improve how we identify and convert high-intent accounts. Our team has a strong in-office culture and works in person 4–5 days per week in San Francisco. We love working together to stay creative and connected, with flexibility to work from home when focus matters most.

Requirements

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 like HubSpot or Salesforce
API integration expertise with experience building workflows using tools like n8n, Zapier, Make, or Tray.io
Deep understanding of PLG metrics with the ability to operationalize activation, engagement, and expansion signals, and a track record of building systems (PQL models, AI agents, predictive analytics) that drove measurable pipeline or revenue
Scrappy builder mindset with the judgment to balance custom builds versus off-the-shelf tools, ideally with experience helping build early data systems fueling a PLG-to-enterprise transition

Nice To Haves

Data warehouse experience (Snowflake, BigQuery, Redshift) and familiarity with dbt or similar transformation tools
Production machine learning experience building, deploying, and monitoring predictive models

Responsibilities

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 for sales
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 combining product behavior, firmographics, and engagement patterns to predict conversion likelihood
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