Gtm Engineer: Data Infrastructure & AI Intelligence

Toast Toast · Enterprise · United States · Remote · Sales : Growth Operations

This role focuses on building and maintaining the data infrastructure and AI intelligence layer for a sales organization, specifically within the CRM and data stack. The primary responsibility is to ensure data integrity, structure, and intelligence to power forecasting, pipeline management, and revenue decision-making. A key aspect involves building AI agents and agentic workflows to transform data processes.

What you'd actually do

  1. Conduct a comprehensive audit of the CRM and data stack, identifying duplicates, stale records, broken field mappings and data gaps
  2. Establish and enforce data governance standards and ownership rules for core CRM objects (Accounts, Contacts, Leads, Opportunities, Activities), including field definitions, required values, and lifecycle states
  3. Define and maintain a canonical data model and data dictionary that aligns GTM teams on consistent terminology, segmentation logic, and record hierarchy (parent/child accounts, territory assignments, etc.)
  4. Design, build, and maintain automated deduplication, normalization, and enrichment plays that create a clean, trusted data layer across the full GTM stack
  5. Integrate third-party enrichment providers to fill data gaps and keep account and contact records current and actionable.

Skills

Required

  • 8+ years in Revenue Operations, Sales Operations, or GTM AI Engineering, with at least 2 years focused on CRM data architecture and infrastructure
  • Deep Salesforce expertise: hands-on experience with data modeling, field configuration, validation rules, flows, and cross-object relationships at scale
  • Demonstrated ability to design and implement end-to-end data pipelines from raw 1st party CRM data entry through normalization, enrichment, deduplication, and reporting-ready output
  • Agent building: Demonstrated experience designing, building, and deploying AI agents and agentic workflows that transformed real work not just using AI, but building with it
  • Strong SQL skills and comfort working directly in a data warehouse environment (Snowflake, BigQuery) for data validation, transformation, and pipeline QA
  • Experience building and owning reporting infrastructure in a BI or dashboard tool (Tableau, Looker, Sigma, Salesforce Reports & Dashboards) with a focus on pipeline and revenue metrics
  • Data governance mindset: you think in systems, not fixes — you build standards, document them, and hold the line on data quality over time.
  • Strong communicator who can translate data concepts for non-technical audiences, including senior Sales and Finance leadership.

Nice to have

  • Experience with data enrichment and identity resolution tools (ZoomInfo, Clearbit, Ringlead, Openprise, or similar).
  • Familiarity with revenue intelligence or sales engagement platforms (Gong, Outreach, Salesloft) and their data integrations with Salesforce.
  • Working knowledge of ETL/ELT tooling (Fivetran, dbt, Airflow) and experience building or maintaining CRM data pipelines in a modern data stack.
  • Experience in a high-growth SaaS or fintech environment with complex multi-product, multi-segment sales motions.

What the JD emphasized

  • Agent building: Demonstrated experience designing, building, and deploying AI agents and agentic workflows that transformed real work not just using AI, but building with it
  • Deep Salesforce expertise: hands-on experience with data modeling, field configuration, validation rules, flows, and cross-object relationships at scale

Other signals

  • AI agents and agentic workflows
  • CRM data ecosystem
  • data governance standards
  • data pipelines