Business Intelligence Engineer, Amazon Robotics

About the role

We are looking for an experienced and enthusiastic Business Intelligence Engineer to join the BI Engineering, Analytics, & Data Science (BAND) team supporting Robotic Storage Tech (RST) programs within Amazon Robotics.

Responsibilities

• 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.
• Collaborate with Data Scientists, Data Engineers, Program Managers, and engineering teams to translate ambiguous new product questions into structured analyses that support milestone decisions.
• 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.
• 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.
• 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.

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