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Rogo

GTM Systems Engineer

New York CityfulltimemidAdded today

About this role

Rogo seeks a GTM Systems Engineer to architect and operate the technology backbone of their revenue organization, owning Salesforce, the surrounding GTM stack, and quote-to-cash workflows while pioneering AI-driven automation in sales processes. This builder-operator role requires deep Salesforce expertise combined with vision for how generative AI transforms deal management, customer success, and revenue operations.

What you'll do

  • Own Salesforce platform end-to-end including data model, Apex/LWC development, Flows, permissions, and DevOps discipline across the revenue lifecycle
  • Architect and maintain integrations between Salesforce and the GTM stack (Gong, Clay, Apollo, Lusha, DocuSign, LinkedIn Sales Navigator, etc.)
  • Design and build AI-native capabilities including account health/scoring models, MEDDPICC analysis, research agents, and MCP servers for safe LLM access to GTM data
  • Own quote-to-cash and CPQ tooling: approval workflows, contract extraction, entitlements, and billing structure controls
  • Manage user provisioning, access controls, and deprovisioning across all GTM systems with role-based design
  • Establish data quality controls, monitoring, and exception reporting as production systems rather than cleanup projects

What they're looking for

  • Salesforce development (Apex, SOQL, Lightning Web Components, Flows)
  • Salesforce administration and configuration
  • API integration and OAuth patterns
  • CPQ and quote-to-cash systems design
  • AI/LLM systems design and evaluation
  • SQL and data modeling
  • RevOps strategy and GTM technology stack evaluation
  • Python or similar for automation and tooling
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Rogo

Rogo builds a financial AI platform with a focus on data-driven insights and user experience. The company is hiring Analytics Engineers to develop data pipelines and reporting tools, and Design Engineers to enhance its design system and user interactions.

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Likely interview questions

  • Tell us about a time you redesigned a Salesforce data model or integration to solve a business problem—what was the leverage point?
  • How would you approach building an AI agent that reads and writes Salesforce data safely, and what guardrails would you put in place?
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