Manager, Data & Analytics, In-store

DoorDash DoorDash · Consumer · New York, NY · 351 In-Store R&D

Manager of Data & Analytics to lead a team responsible for data products, AI systems, and reporting infrastructure. The role involves people leadership, owning the data roadmap, partnering with stakeholders, and contributing to AI developer infrastructure. Key responsibilities include leading a team, owning the D&A roadmap, translating business problems into technical scope, designing prompts, integrating with systems via APIs, and contributing to AI developer infrastructure.

What you'd actually do

  1. Lead, coach, and grow a team of senior analysts and BI engineers — running performance, career development, and hiring end-to-end.
  2. Own the D&A roadmap across GTM, Product, Finance, and Partnerships, balancing the team's investment between predictive AI/ML systems, traditional reporting, and cross-functional analytics.
  3. Act as the senior data voice in executive conversations — translating business problems into technical scope, pushing back when a request is the wrong shape, and influencing how leaders across segments make decisions.
  4. 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.
  5. Partner closely with GTM Engineering, GTM Systems, and Revenue Insights to define a clean "Build vs. Run" operating model and unlock the next generation of AI-powered GTM products.

Skills

Required

  • SQL
  • DBT
  • Snowflake or BigQuery
  • Looker or equivalent BI tooling
  • DBT modeling at scale
  • Salesforce-to-warehouse pipelines
  • people management experience
  • modern AI and ML approaches
  • LLM-powered workflows
  • GenAI tooling ecosystem

Nice to have

  • modern cloud data warehouse
  • enterprise data infrastructure
  • modern AI and ML approaches
  • predictive modeling
  • LLM-powered workflows
  • GenAI tooling ecosystem
  • production AI systems

What the JD emphasized

  • AI systems
  • predictive AI/ML systems
  • LLM-powered workflows
  • production AI systems
  • AI developer infrastructure
  • GTM AI products

Other signals

  • GenAI agents that audit Salesforce in real time
  • churn and ICP models that drive CSM and AE prioritization
  • AI-powered GTM products
  • LLM-powered workflows
  • production AI systems