Senior Systems Engineer, Commercial Intelligence and Analytics

The Trade Desk The Trade Desk · Media · Los Angeles, CA · Business Intelligence

This role focuses on building and maintaining production systems that leverage AI and LLM capabilities to provide commercial intelligence and analytics for internal teams, particularly Sales and Client Services. The engineer will design, develop, and deploy scalable data systems, insight pipelines, and automated workflows, integrating AI/LLM features to improve decision-making, prioritization, and operational efficiency. The role emphasizes the engineering lifecycle from requirements to production, ensuring reliability, explainability, and impact of the delivered intelligence systems.

What you'd actually do

  1. Own and maintain the production systems that surface revenue performance, portfolio health, renewal dynamics, client opportunity, and service risk so internal teams can act on the right priorities with confidence.
  2. Design, build, and operate proactive insight pipelines, translating recurring business needs into system architecture, data models, signal logic, and production-ready outputs so intelligence can be delivered consistently at scale.
  3. Work directly with Business Technology and Revenue Operations to integrate intelligence outputs into dashboards, alerting systems, and operational workflows so signals reach users through the tools where decisions are made.
  4. Build and deploy tools, signals, and automated deliverables that help teams prioritize effort, identify risk and opportunity, and act with greater precision so commercial resources are directed where they matter most.
  5. Build trusted, production-grade data assets and pipelines using SQL, Python, validated metrics, and well-documented logic so downstream systems remain reliable, explainable, and maintainable.

Skills

Required

  • 5+ years of experience in analytics engineering, data engineering, software engineering, or a similarly systems-oriented role with strong quantitative foundations.
  • Strong SQL skills and experience working with large, complex datasets in modern data environments.
  • Proficiency in Python for building production systems, automation, and data pipeline development.

Nice to have

  • LLM APIs
  • Docker
  • Git/GitLab (CI/CD)
  • Jira
  • Confluence
  • Tableau

What the JD emphasized

  • production systems
  • scalable data systems
  • automated workflows
  • production-grade systems
  • production
  • scalable system investments

Other signals

  • AI-assisted workflows
  • integrate AI and LLM capabilities
  • operationalize intelligence into durable, production-grade systems