Business Intelligence Engineer (gtm), In-store Data & Analytics

DoorDash DoorDash · Consumer · San Francisco, CA · 351 In-Store R&D

This role focuses on building and shipping production GenAI agents and automations for internal use within a B2B SaaS company. The engineer will translate GTM problems into AI-powered workflows, integrate with existing GTM tools, and contribute to shared AI developer infrastructure. The role requires experience in building production software, deploying LLM applications, and proficiency in Python, SQL, and GCP.

What you'd actually do

  1. Build and ship production AI agents and automations on Google Cloud Platform (BigQuery, Cloud Functions, Compute Engine, Secret Manager, Terraform, VertexAI) that directly touch the daily workflow of every AE, AM, and CSM at SevenRooms.
  2. Translate narrow, well-defined GTM problems into shipped solutions — designing prompts, integrating with Salesforce and our GTM stack via APIs, and instrumenting the leading indicators that prove ROI.
  3. Partner with frontline reps and CSMs as design partners — co-designing solutions, iterating in production, and driving real adoption rather than dashboards nobody opens.
  4. Collaborate cross-functionally with Revenue Operation), Strategy & Operations, Product, Enablement, Customer Success, Sales, and FP&A to surface the right problems and build solutions that stick.
  5. Contribute to the team-wide AI developer infrastructure (skills libraries, MCP integrations, prompt frameworks) that makes every D&A engineer and analyst faster at deploying AI.

Skills

Required

  • Python
  • SQL
  • GCP (BigQuery, Cloud Functions, Compute Engine, Secret Manager)
  • Terraform
  • VertexAI
  • Salesforce APIs (SFDC)
  • prompt engineering
  • evaluation
  • integration patterns
  • DBT
  • Fivetran
  • REST APIs

Nice to have

  • Gainsight
  • Outreach
  • Gong

What the JD emphasized

  • production AI agents
  • production systems
  • production software
  • LLM-powered applications in production

Other signals

  • building AI-powered internal products
  • ship production AI systems
  • build the AI-powered workflows and proprietary internal tools
  • design, build, and ship production GenAI agents and automations
  • deploying LLM-powered applications in production