About this role
Are you passionate about using data to drive business decisions at massive scale? EU Vendor Intelligence is looking for a Business Intelligence Engineer to help power Amazon's growth and profitability across European markets. In this role, you will tackle difficult, loosely defined business problems across key retail programs including Selection and Vendor Management. You will partner closely with Program Managers and Vendor Managers — not just answering their questions, but refining problems, recommending alternatives, and anticipating future analytical needs. You will own mid-to-large BI solutions end-to-end: from data modelling and pipeline design to dashboards and deep-dive analyses that drive real business decisions. What you'll do: • Design robust, maintainable data models and pipelines that power reporting and analytics for EU-wide retail programs • Write advanced SQL and leverage scripting (e.g. Python) to solve difficult analytical problems and automate BI processes • Build compelling visualizations and self-service reporting that reduce bottlenecks and put data directly into stakeholders' hands • Develop a deep understanding of data lineage — from source systems through metrics aggregation to how business intelligence is consumed and acted upon • Collaborate with cross-functional teams (PMs, VMs, Data Engineers, other BIEs) to deliver solutions that balance business needs with technology constraints • Improve code quality, optimize BI processes for speed, cost, and reliability, and mentor teammates This is a high-visibility role where your analytical rigor and business acumen will directly influence how Amazon grows selection and manages vendor relationships across Europe. You'll work in an environment that values high-quality, well-documented solutions — and gives you the autonomy to deliver them. Key job responsibilities • Own the design, build, and maintenance of mid-to-large BI solutions — data models, ETL pipelines, dashboards, and analyses — supporting EU Stores retail programs such as Selection and Vendor Management • Write advanced SQL and Python to solve difficult, loosely defined analytical problems and automate recurring BI processes • Develop and maintain a deep understanding of data lineage: source systems, metrics aggregation logic, and how business intelligence is consumed and acted upon by stakeholders • Partner with Program Managers, Vendor Managers, and cross-functional teams to refine business requirements, recommend analytical approaches, and anticipate future needs • Build self-service reporting and visualization solutions that reduce bottlenecks and empower business partners to access data independently • Ensure all solutions are efficient, testable, maintainable, well-documented, and resistant to data quality issues • Optimize BI processes for speed, cost, and reliability; establish data and reporting SLAs where appropriate • Mentor junior team members and contribute to team design, scoping, and prioritization discussions A day in the life You might start your morning reviewing a data pipeline you built, checking for anomalies before a weekly business review. By mid-morning, you're in a working session with a Vendor Manager, refining requirements for a new selection coverage analysis. After lunch, you're writing SQL to model vendor performance data, then building a QuickSight dashboard that lets Program Managers self-serve insights without waiting on ad-hoc requests. Later, you review a teammate's code, suggest a cleaner data model, and wrap up by automating a manual reporting step that's been slowing the team down. About the team The EU Vendor Intelligence Team builds BI solutions and capabilities that empower business users to self-serve their analytics and data needs across EU Stores. We support a wide variety of programs — from selection growth and vendor management to vendor education and vendor-facing chatbot tools. Our mission is to make data accessible, reliable, and actionable so that Program Managers and Vendor Managers can make faster, better-informed decisions at scale. We value automation over manual work, clean data models over quick fixes, and strong partnerships with the business teams we serve.