Business Intelligence Engineer, GEMS NA Startup

At Amazon, we are building a procurement organization powered by trusted, scalable, and AI-ready data. The Global Engineering, Maintenance, & Sustainability (GEMS) Organization is seeking a Business Intelligence Engineer to support drive visibility, compliance analytics, and vendor performance insights across global procurement. This role focuses on transforming fragmented operational data into standardized, governed analytical products. You will work on high-impact initiatives such as sourcing analytics, compliance monitoring, and vendor management reporting, while contributing to the foundation for scalable self-service and AI-enabled decision making. This is a hands-on role for someone who can partner with business stakeholders, build robust data models, and deliver clear, actionable insights in a fast-paced environment. Key job responsibilities - Own and deliver BI solutions (datasets, dashboards, and analyses) supporting sourcing, compliance, and vendor management programs - Contribute to the development of governed, reusable data products aligned with the GPO Data Hub - Partner with business stakeholders to define metrics, translate requirements, and drive adoption of standardized reporting - Build and maintain scalable data models and ETL pipelines using SQL and Python on AWS - Ensure data quality, consistency, and documentation across analytical outputs - Develop intuitive visualizations that enable clear decision-making for procurement leaders - Collaborate with cross-functional teams within GPT and BI&A to align on data definitions, architecture, and best practices - This position is based out of a corporate office and requires approximately 25% national travel A day in the life You will work closely with GEMS stakeholders to understand sourcing and operational processes, define metrics, and build analytical solutions that improve visibility and decision-making. You will balance business-facing work (requirements, alignment, adoption) with technical development (data modeling, pipelines, dashboards), collaborating with teams across regions and functions to deliver scalable and reliable analytics.