About this role
The Everyday Essentials (EE) Replenishment team is looking for a Business Intelligence Engineer III to lead analytics strategy and drive insights that power one of Amazon's largest subscription programs, Subscribe and Save (SnS), and the broader EE reorder ecosystem. You will own end-to-end analytics for critical business initiatives, define measurement frameworks for new product launches, and build the data infrastructure that enables leadership and cross-functional teams to make informed decisions at scale. In this role, you will lead analytics for high-visibility programs including customer-facing AI experiences (Rufus/Alexa integration with SnS), GenAI-powered seller tools (EE Advisor), and predictive models that proactively identify and resolve subscription fulfillment risks. You will own the analytics roadmap for the EE Replenishment DnA function, drive operational excellence across data pipelines and reporting infrastructure, and mentor junior BIEs. You will define P0/P1 metrics, build production-grade data pipelines, and translate complex business questions into quantifiable insights and scalable reporting that inform VP-level business reviews. The ideal candidate combines deep technical expertise in large-scale data engineering with the ability to independently identify high-impact opportunities, design analytical frameworks, and influence product and business strategy through data. You are comfortable operating across predictive analytics, ML model design, and traditional BI, and can context-switch between building infrastructure, conducting deep-dive analyses, and presenting to senior leadership. Key job responsibilities - Own the analytics strategy and roadmap for EE Replenishment, defining priorities across SnS metrics, reorder analytics, incentive measurement, and cost-to-serve optimization - Lead analytics for GenAI and ML initiatives including EE Advisor (multi-agent seller analytics system) and predictive models for offer risk and subscription health - Design, build, and maintain automated data pipelines using Andes, Cradle (Spark), EMR, Lambda, and AWS services to support reorder metrics, selection health reporting, and business reviews (WBR/MBR/QBR) - Define and operationalize P0/P1 reorder metrics that measure program effectiveness across selection, incentives, customer retention, and cost-to-serve - Own experiment measurement frameworks including APT metric onboarding, enabling self-serve experiment analysis at scale across the org - Build and maintain QuickSight dashboards and self-service reporting tools used by VP+ leadership, product, category, and finance teams - Conduct deep-dive analyses on customer behavior, subscription churn, reorder patterns, program ROI, and escalation investigations - Drive operational excellence across the BI function: pipeline health, table migrations, reporting standardization, and KTLO reduction - Mentor and develop junior BIEs, lead sprint planning and prioritization, and coordinate cross-functional analytics workstreams - Partner with science teams on ML model design, feature development, and evaluation for recommendation and prediction systems A day in the life The EE Replenishment BI team powers analytics for Subscribe and Save and the broader EE reorder ecosystem, a $23B+ OPS business. You will shape how Amazon measures and optimizes the reorder flywheel, from subscription acquisition and retention to cost-to-serve and incentive ROI. Current priorities include measurement frameworks for Buy Again and Save, scaling EE Advisor (GenAI-powered seller analytics), and predictive models that catch fulfillment failures before they reach customers. This is a rare opportunity to combine large-scale data engineering with ML, GenAI, and direct business influence in a space touching hundreds of millions of customers. About the team You start your morning reviewing SnS order health dashboards before the weekly business review. Mid-morning, you partner with product and science to design an experiment framework for a new reorder incentive, then build the Cradle pipeline to measure it. After lunch, you deep-dive into why subscription churn spiked in a specific cohort, presenting findings to your Sr. Manager with a recommendation. Later, you mentor a BIE on pipeline design, review a PR for a QuickSight dashboard, and prep the analytics section of an upcoming VP review. Your stakeholders span product, engineering, science, finance, and category teams.