Senior Business Intelligence Engineer, Devices Demand Science Optimization

About the role

We build the data infrastructure and analytics that powers decision making across demand planning, forecasting, sales, and inventory for Amazon Devices globally. Our core tables serve as the single source of truth for metrics used in leadership reviews, business planning cycles (OP1, OP2, QxG), and High Velocity Events like Prime Day. We are actively building AI enabled tools, including chat agents and automated reporting, and migrating our reporting to Quick. Our team values ownership, quality, and thinking big. We move fast, ship real products, and work closely with PL leaders, finance, science, and engineering teams to deliver insights that matter.

Requirements

• 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
• 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
• Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
• USA, WA, Seattle - 130,400.00 - 176,300.00 USD annua

Additional Information

We're looking for a Senior Business Intelligence Engineer (BIE) to build AI-native data products for Amazon Devices. You'll architect the semantic and contextual layers that make our business data machine-readable, designing MCP servers, tool schemas, and retrieval pipelines that let AI agents reason over sales, inventory, and demand planning data autonomously. You'll also own the underlying core tables and pipelines that feed both human dashboards and agent workflows. This role sits at the intersection of data engineering, applied AI, and analytics product development. You'll define how machines understand and act on our business data, and scale that intelligence across multiple BI teams. Key job responsibilities - 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. - 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. - Develop AI-powered automation for recurring business workflows: WBR narratives, flash reports, executive summaries, anomaly callouts, and forecast commentary using LLMs and agentic patterns. - Build and maintain QuickSight dashboards for self-service analytics while progressively shifting routine queries to agent-driven interfaces. - Implement data quality frameworks: automated validation, drift detection, monitoring, and alerting. Ensure reliability for both human consumers and AI systems. - Design pipeline orchestration across multiple systems and schedules, optimizing for freshness, cost, and downstream dependency management. - Partner with demand planning, forecasting, finance, and inventory teams to translate business requirements into well-scoped, AI-augmented data solutions. - Evaluate and integrate emerging AI/ML capabilities (RAG, function calling, multi-agent systems) into the BI stack, establishing patterns other teams can adopt.

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