Business Intelligence Engineer, Supply Chain Execution Team

Amazon Amazon · Big Tech · Nashville, TN · Business Intelligence

This role focuses on building the analytical foundation for a supply chain network, including data pipelines, dashboards, and machine learning models to support data-driven decision-making. It involves defining analytical frameworks, evaluating plan convergence, auditing data inputs, and partnering with optimization and operations teams to identify and quantify root causes for deviations from network goals.

What you'd actually do

  1. Define and maintain analytical frameworks that translate operational knowledge into quantified target states across fulfillment network domains, working with supply chain partners to document what good performance looks like and how to measure distance from target
  2. Build and support data pipelines and dashboards that evaluate whether plans are converging toward target states, including automated reporting on plan deviations, constraint violations, and execution misses with root-cause attribution
  3. Audit upstream data inputs that feed the planning chain—including transit time configurations, forecast accuracy, inventory health, and capacity assumptions—to ensure the analytical foundation remains reliable
  4. Partner with supply chain optimization and operations teams to surface where system outcomes diverge from network-level goals, quantify the impact, and support investigation into root causes
  5. Contribute to evolving our measurement systems as the network changes, updating target-state definitions and detection methodologies to stay relevant as new fulfillment centers open and operational patterns shift

Skills

Required

  • analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience
  • SQL, ETL or Oracle experience
  • processing large, multi-dimensional datasets from multiple sources experience
  • performing statistical analysis experience
  • developing automated reporting experience
  • data visualization using Tableau, Quicksight, or similar tools
  • data modeling, warehousing and building ETL pipelines
  • SQL to pull data from a database or data warehouse
  • scripting experience (Python) to process data for modeling

Nice to have

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

What the JD emphasized

  • machine learning models