Sr. Business Intelligence Engineer, AWS Analytics Engineering
Pubblicato il 02-10-2026 - Amazon in Milano
Sr. Business Intelligence Engineer, AWS Analytics Engineering
Job ID: 10538977 | Amazon Development Center U.S., Inc.
AWS is looking for a Sr. Business Intelligence Engineer to join the AWS Analytics Engineering (AAE) team supporting Amazon Quick. Formerly Amazon QuickSight, Quick has evolved from a standalone BI service into a comprehensive, generative-AI-powered business intelligence platform that combines traditional analytics with modern AI assistance. It brings together two complementary experiences. Amazon Quick Sight is the cloud-native dashboarding and visualization engine that powers governed datasets, interactive dashboards, and ML-driven insights such as forecasting and anomaly detection. Alongside it, an agentic AI layer lets users chat with their data, conduct deep research, and automate multi-step workflows across connected enterprise sources through natural language. Available in the browser, as a desktop companion, and through extensions for Slack and Microsoft Office, Quick is used to turn data into decisions and actions, without requiring machine learning expertise.
This is your opportunity to shape the analytical foundation of one of AWS’s fastest-evolving products.
As a Sr. BIE on this team, you will own the end-to-end analytical roadmap for Amazon Quick. You will generate insights that directly drive adoption, engagement, and growth, and that influence product and feature decisions at the leadership level. You will work with datasets in one of the world's largest data warehousing environments, developing and supporting your hypotheses with data-driven analysis. You will partner closely with product, business, finance, and engineering leaders to tackle non-standard, ambiguous business problems and translate them into actionable output. You will be part of a business intelligence team that goes beyond reporting and drills into the drivers behind key business metrics, redefining best practices with a cloud-based approach to scalability and automati