GTM Engineer - Data Platform

About the role

• Modal is building a new infrastructure layer for AI, providing instant GPU access, sub-second container starts, and native storage for low-latency inference, model fine-tuning, and production-ready sandboxes at scale.
• Customers include Ramp, Cognition, DoorDash, and Suno. Modal has passed $1 billion in annualized run rate, up 15x from a year ago, and raised a $750 million Series D led by Accel in October 2026 at a $15.75 billion valuation.
• The team includes creators of open-source projects such as Seaborn and Luigi, academic researchers, international olympiad medalists, and experienced engineering and product leaders.
• GTM Engineering owns the data, business logic, workflows, and agents that enable Marketing and Sales to help customers get the most out of Modal. The team builds behind-the-scenes systems that anticipate customer needs and help customers grow on Modal.
• The team brings a product management, growth engineering, and data science approach to GTM, from centralizing the data platform to building account-research agents.
• Own Modal's GTM data platform in a role combining analytics engineering with revenue operations. Build the organization map, people graph, and data systems that discover future customers and support GTM workflows, partnering closely with the Data team on the shared foundation.

Responsibilities

• Build and maintain an organization map and people graph connecting companies, labs, teams, and individuals to identify researchers, engineers, and early-stage startups, including people beyond existing users and the CRM.
• Own identity resolution, enrichment, and CRM schemas so affiliations, roles, and relationships remain accurate, current, and traceable to their sources.
• Build GTM data models in Snowflake and dbt connecting people, companies, workspaces, workloads, usage, and revenue, giving representatives and agents a shared foundation for customer context.
• Partner with RevOps and Marketing to define and implement metrics, data models, and business rules for routing, territory assignments, compensation, pipeline reporting, and attribution.
• Partner with Data on usage forecasts and account signals. Build reproducible reporting that reconciles actuals, forecasts, human adjustments, and credited attainment.
• Build reverse ETL pipelines for ad audiences, lifecycle campaigns, CRM enrichment, outbound, and event outreach, preserving engagement history and campaign eligibility.
• Own data quality and system health, including graph coverage, stale enrichment, failed syncs, and historical reporting accuracy.
• Build tooling that helps GTM Engineering ship reliably, including development environments, automated tests, deployments, backups, and shared AI agents and skills.

Requirements

• SQL fluency and Python proficiency.
• Already use AI coding tools and agents to build, debug, and automate work, and help teammates do the same.
• Experience building data models and production pipelines with tools such as Snowflake, dbt, Modal, or Hex.
• Hands-on experience with CRM architecture, integrations, and revenue or marketing operations.
• Understanding of how leads, accounts, opportunities, and revenue move through a business. Experience with usage-based businesses is a plus.
• High attention to detail and a habit of testing, documenting, and monitoring the systems you build.