GTM Engineer for Data Infrastructure & AI Intelligence

About the Role

The Sales Operations team at Toast is looking for a GTM Engineer for Data Infrastructure & AI Intelligence. In this foundational role, you will be the architect of Toast's sales data ecosystem, owning the integrity, structure, and intelligence layer of the CRM and data stack. Your work will ensure that every sales representative, manager, and executive operates from a single source of truth, building a clean data foundation that powers forecasting, pipeline management, and critical revenue decision-making across the company.

Responsibilities

• Conduct a comprehensive audit of the CRM and data stack, identifying duplicates, stale records, broken field mappings, and data gaps.
• 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.
• 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.).
• Design, build, and maintain automated deduplication, normalization, and enrichment plays that create a clean, trusted data layer across the full GTM stack.
• Integrate third-party enrichment providers to fill data gaps and keep account and contact records current and actionable.
• Implement ongoing data health monitoring with automated alerts and SLA-driven remediation workflows to prevent degradation from impacting reps or reporting.
• Build and maintain pipeline dashboards, activity data models, and stage progression metrics that provide real-time visibility into revenue performance.
• Partner with Finance and RevOps on forecasting models, ensuring underlying data is accurate, consistently defined, and reconcilable across systems (CRM, data warehouse).
• Serve as the authoritative data partner for QBR prep, board reporting, and ad hoc revenue analyses, bridging raw system data and executive-ready insights.
• Partner with Sales, Marketing, and Finance stakeholders to surface data quality issues at the source, build shared accountability, and close gaps in activity capture (calls, emails, meetings) so sellers' work is accurately reflected in coverage and productivity metrics.

Requirements

• 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.
• 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: ability to think in systems, build standards, document them, and maintain data quality over time. Working understanding of data privacy regulations and compliance is a plus.
• Strong communicator who can translate data concepts for non-technical audiences, including senior Sales and Finance leadership.

Nonessential Skills/Nice to Haves

• 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.