Business Intelligence Engineer, Amazon Robotics

Key job responsibilities

• Statistical Analysis for New Products: Execute foundational statistical methods including hypothesis testing, confidence intervals, design of experiments, and regression analysis to evaluate new containerized and workcell system performance against readiness criteria at each development phase.
• Stakeholder Partnership: Collaborate with Data Scientists, Data Engineers, Program Managers, and engineering teams to translate ambiguous new product questions into structured analyses that support milestone decisions.
• Data Source Development: Identify and onboard data sources from new containerized and workcell systems as they come online, define NPI-relevant metrics, and build data pipelines that enable performance tracking.
• Whitepaper & Insights Delivery: Author recommendation-focused whitepapers and deep-dive analyses that inform new product development decisions — including system configuration trade-offs, readiness assessments, and deployment strategy recommendations.
• BI Ownership: Design, build, and maintain dashboards and reporting solutions that track new containerized and workcell products through their Alpha, Beta, and General Availability phases, providing visibility into system maturation and operational readiness.

About the team

Robotic Storage Tech (RST) invents, designs, and develops transformational storage solutions used in the Amazon Logistic Network. We will realize the next generation of automated containerized systems, and optimize our existing install base. We are a team of talented engineers, product evangelists, and creators focused on technologies that make inventory pick, stow, and count safer and more efficient for Amazon. We deliver value with our elite robotic technologies because we listen and respond to our many customers, develop robust and reliable solutions, and invest in both our people and our products.

Basic Qualifications

• 3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience
• Experience with data modeling, warehousing and building ETL pipelines
• Experience with data visualization using Tableau, Quicksight, or similar tools
• Experience writing complex SQL queries
• Experience in Statistical Analysis packages such as R, SAS and Matlab
• Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling

Preferred Qualifications

• Experience with AWS solutions such as EC2, DynamoDB, S3, and Redshift
• Experience in data mining, ETL, etc. and using databases in a business environment with large-scale, complex datasets