Senior Business Intelligence Engineer, Devices Demand Science Optimization

Amazon Amazon · Big Tech · Seattle, WA · Business Intelligence

Senior Business Intelligence Engineer to build AI-native data products for Amazon Devices, architecting semantic and contextual layers for AI agents to reason over business data. Responsibilities include data pipelines, MCP servers, semantic layers for AI agents, AI-powered automation for business workflows, and evaluating/integrating emerging AI/ML capabilities.

What you'd actually do

  1. Architect and maintain production data pipelines (Iceberg, Redshift, Athena) that serve as the single source of truth for Devices sales, inventory, pricing, and demand planning metrics.
  2. Design and build MCP servers and semantic layers that expose core tables, metrics, and business logic to AI agents, including schema documentation, tool definitions, and retrieval-augmented context.
  3. Develop AI-powered automation for recurring business workflows: WBR narratives, flash reports, executive summaries, anomaly callouts, and forecast commentary using LLMs and agentic patterns.
  4. Build and maintain QuickSight dashboards for self-service analytics while progressively shifting routine queries to agent-driven interfaces.
  5. Implement data quality frameworks: automated validation, drift detection, monitoring, and alerting. Ensure reliability for both human consumers and AI systems.

Skills

Required

  • 10+ years of professional or military experience
  • 7+ years of SQL, ETL or Oracle experience
  • 7+ years of processing large, multi-dimensional datasets from multiple sources experience
  • 5+ years of developing automated reporting experience
  • Experience with AWS technologies
  • Experience in scripting for automation (e.g. Python) and advanced SQL skills.
  • Experience with data visualization using Tableau, Quicksight, or similar tools
  • Knowledge of data warehousing and data modeling
  • Experience working directly with business stakeholders to translate between data and business needs

Nice to have

  • Experience managing, analyzing and communicating results to senior leadership
  • Experience programming to extract, transform and clean large (multi-TB) data sets
  • Experience with theory and practice of information retrieval, data science, machine learning and data mining

What the JD emphasized

  • AI agents
  • agentic patterns
  • AI-augmented data solutions
  • AI/ML capabilities
  • AI enabled tools
  • chat agents
  • agent-driven interfaces

Other signals

  • AI agents reasoning over data
  • AI-powered automation for business workflows
  • Integrating RAG, function calling, multi-agent systems