Senior Business Intelligence Engineer, Everyday Essentials Replenishment

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.

- 5+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience - 3+ years of data warehouse technical architectures, data modeling, infrastructure components, ETL/ ELT and reporting/analytic tools and environments, data structures and hands-on SQL coding experience - 3+ years of processing large, multi-dimensional datasets from multiple sources experience - Master's degree, or PhD and 3+ years of hands-on predictive modeling and large data analysis experience - 3+ years of developing automated reporting experience - Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, Statistics, Economics, or a related field - Knowledge of data visualization tools such as Quick Sight, Tableau, Power BI or other BI packages - Experience with data modeling, warehousing and building ETL pipelines - Experience with data scripting languages (e.g., SQL, Python, R, or equivalent) or statistical/mathematical software (e.g., R, SAS, Matlab, or equivalent)

- Master's degree or above in a quantitative field - Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions - Master's degree, or PhD and 5+ years of building machine learning models for business application experience - Experience with large scale distributed systems such as Hadoop, Spark etc. - Experience as a mentor, tech lead or leading an engineering team - Experience building measures and metrics, and developing reporting solutions - Experience using complex financial models, KPIs, and data analysis to shape long-term business strategy and influence senior leadership decisions, with proven, measurable impact at an organisational level (e.g., financial savings, operational improvements, or customer benefits)

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, WA, Seattle - 130,400.00 - 176,300.00 USD annually