Business Intelligence Engineer II, SCOT - Long Term Planning and Forecasting

About the role

The Supply Chain Optimization Technology (SCOT) team owns Amazon's global inventory planning systems. SCOT LTPF is responsible for long-term forecasting and planning across Topline, Inventory, and Capacity — helping Amazon plan key metrics including ordered units and GMS, inventory units and cost, and FC building capacity and topology (size and location).

Responsibilities

• Develop and maintain analytical dashboards, reports, and data pipelines using Amazon QuickSight, SQL, and AWS services to track forecast performance across Topline, Inventory, and Capacity planning — following established architectural patterns and team standards to ensure solutions are reliable and well-documented
• Collaborate with scientists, engineers, and product managers to gather requirements and deliver data solutions that surface root-cause insights — escalating trade-offs and prioritization decisions to senior team members when needed
• Build and maintain data models, ETL processes, and automated reporting mechanisms that enable data-driven decision making — optimizing for performance and reliability within the team's existing frameworks
• Leverage generative AI tools and techniques to accelerate insight generation and automate reporting workflows, contributing ideas for new AI-driven improvements to the team's analytical processes
• Conduct deep-dive analyses to identify trends, patterns, and opportunities for improvement — delivering clear findings on well-scoped business questions and contributing to analyses on more complex or ambiguous problems with guidance from senior team members
• Proactively identify data quality issues, dependencies, and bottlenecks — troubleshooting and resolving issues within scope and escalating broader systemic problems with clear documentation
• Own and deliver medium-complexity projects from start to finish — developing project plans, tracking milestones, and communicating findings clearly to team leads and stakeholders
• Accept and champion team best practices in data modeling, metric definitions, and code quality — proactively identifying opportunities to improve processes and tools within your area of ownership
• Support onboarding of new team members and contribute to knowledge-sharing initiatives; participate in hiring loops and technical assessments as a bar raiser candidate (with development support)

Qualifications

• 3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience
• 1+ years of SQL, ETL or Oracle experience
• 1+ years of processing large, multi-dimensional datasets from multiple sources experience
• 1+ years of performing statistical analysis experience
• 1+ years of developing automated reporting experience
• Experience with data visualization using Tableau, Quicksight, or similar tools
• Experience with data modeling, warehousing and building ETL pipelines
• 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