Sr. Business Intelligence Engineer

DoorDash DoorDash · Consumer · Boston, MA · 351 In-Store R&D

This role focuses on building and maintaining data pipelines and infrastructure that support AI capabilities within an enterprise environment. The engineer will establish API connections, model and surface data, and architect automated AI flows, with a significant focus on integrating SevenRooms' data ecosystem into DoorDash's broader organization. Experience with deploying AI models and building scalable systems for AI capabilities is required.

What you'd actually do

  1. Build foundational AI infrastructure and data pipelines that directly shape the scalability and productivity of the entire organization
  2. Work across a modern, expansive tech stack including DBT, Looker, BigQuery, Fivetran, Census, Cloud Functions, Salesforce, NetSuite, and more
  3. Serve as a strategic link between SevenRooms and DoorDash, integrating systems and data in ways that haven't been tackled yet
  4. Partner closely with stakeholders across every department — GTM, Finance, Operations, and beyond — to surface data that drives real decisions
  5. Own governance and control mechanisms that ensure accuracy and reliability across all data pipelines

Skills

Required

  • 3+ years of hands-on experience with DBT
  • 1 year owning and architecting production-grade data pipelines end-to-end
  • 4+ years owning and administering a BI tool such as Looker, Tableau, or Mode
  • 4+ years of demonstrated SQL mastery
  • 2+ years of Python experience applied to real-world data challenges, including API integrations, data extraction, and workflow orchestration via Airflow or Cloud Composer
  • experience deploying or launching AI models in a business context
  • ability to build scalable systems that teams across the business can rely on with confidence

Nice to have

  • DBT
  • Looker
  • BigQuery
  • Fivetran
  • Census
  • Cloud Functions
  • Salesforce
  • NetSuite
  • Airflow
  • Cloud Composer

What the JD emphasized

  • deploying or launching AI models in a business context
  • building the infrastructure needed to scale those capabilities further

Other signals

  • Build foundational AI infrastructure and data pipelines
  • architecting automated AI flows
  • building the underlying systems that enable AI capabilities
  • deploying or launching AI models in a business context