Business Intelligence Engineer, Everyday Essentials Replenishment
Posted Updated
The Everyday Essentials Replenishment team is looking for a Business Intelligence Engineer to drive analytics and insights that power one of Amazon's largest subscription programs, Subscribe and Save (SnS). You will work at the intersection of data engineering, analytics, and product strategy to measure, optimize, and scale the mechanisms that keep millions of customers stocked on their everyday essentials.
In this role, you will own end-to-end analytics for customer-facing AI experiences (Rufus/Alexa for Shopping integration with SnS), build and maintain metrics that measure reorder behavior across Everyday Essentials, and develop data pipelines and dashboards that inform leadership decisions on selection, incentives, and customer engagement. You will partner closely with product managers, software engineers, data scientists, and program managers to translate ambiguous business questions into quantifiable insights and scalable reporting infrastructure.
The ideal candidate is comfortable working across large-scale datasets (billions of records), can build production-grade data pipelines, and thrives in turning messy real-world data into clear narratives that drive action.
Key job responsibilities
- Own analytics for SnS integration with conversational AI surfaces (Rufus, Alexa for Shopping): define success metrics, build measurement frameworks, analyze experiment results, and report on customer engagement and conversion
- Design, build, and maintain automated data pipelines using Andes, Cradle (Spark), and AWS services to support reorder metrics, selection health reporting, and business reviews (WBR/MBR/QBR)
- Develop and maintain QuickSight dashboards and self-service reporting tools used by leadership, product, and category teams
- Define and operationalize P0/P1 reorder metrics that measure program effectiveness across selection, incentives, and customer retention
- Conduct deep-dive analyses on customer behavior, subscription churn, reorder patterns, and program ROI to inform roadmap prioritization
- Partner with science teams on model evaluation and feature development for recommendation and personalization systems
- Contribute to the team's data architecture strategy, including data modeling, pipeline reliability, and cost optimization
In this role, you will own end-to-end analytics for customer-facing AI experiences (Rufus/Alexa for Shopping integration with SnS), build and maintain metrics that measure reorder behavior across Everyday Essentials, and develop data pipelines and dashboards that inform leadership decisions on selection, incentives, and customer engagement. You will partner closely with product managers, software engineers, data scientists, and program managers to translate ambiguous business questions into quantifiable insights and scalable reporting infrastructure.
The ideal candidate is comfortable working across large-scale datasets (billions of records), can build production-grade data pipelines, and thrives in turning messy real-world data into clear narratives that drive action.
Key job responsibilities
- Own analytics for SnS integration with conversational AI surfaces (Rufus, Alexa for Shopping): define success metrics, build measurement frameworks, analyze experiment results, and report on customer engagement and conversion
- Design, build, and maintain automated data pipelines using Andes, Cradle (Spark), and AWS services to support reorder metrics, selection health reporting, and business reviews (WBR/MBR/QBR)
- Develop and maintain QuickSight dashboards and self-service reporting tools used by leadership, product, and category teams
- Define and operationalize P0/P1 reorder metrics that measure program effectiveness across selection, incentives, and customer retention
- Conduct deep-dive analyses on customer behavior, subscription churn, reorder patterns, and program ROI to inform roadmap prioritization
- Partner with science teams on model evaluation and feature development for recommendation and personalization systems
- Contribute to the team's data architecture strategy, including data modeling, pipeline reliability, and cost optimization