Business Intelligence Engineer, Intermodal

About the Role

Amazon's Intermodal (AZIM) Network Optimization team is looking for a Business Intelligence Engineer to build the data foundation and analytical tooling that powers cost optimization across the intermodal network. This is a hands-on role responsible for transforming complex, high-volume supply chain data into automated pipelines, self-service dashboards, and deep-dive analyses that surface cost reduction opportunities and drive operational decisions. Working closely with product managers, supply chain managers, and business stakeholders, you'll own the end-to-end analytics lifecycle — from data modeling and ETL development to visualization and insight generation. The ideal candidate combines strong technical skills in SQL, data engineering, and statistical analysis with genuine curiosity about the business, and thrives in a fast-paced environment where the quality and speed of your analytics directly shape where the network invests to reduce cost and improve efficiency.

Key Responsibilities

• Data Pipeline & Infrastructure Design: Design, build, and maintain scalable ETL pipelines and data models that consolidate intermodal cost, volume, and operational data into reliable, query-ready datasets.
• Dashboards & Self-Service Analytics: Develop and maintain automated dashboards and reporting mechanisms that give stakeholders real-time visibility into cost drivers, network performance, and optimization opportunities — reducing manual reporting effort.
• Deep-Dive Analysis: Conduct rigorous analyses on cost trends, operational inefficiencies, and network anomalies to identify root causes and quantify savings opportunities. Translate raw data into clear, actionable recommendations.
• Metrics & Data Quality: Define, instrument, and monitor key business metrics. Establish data quality checks and validation frameworks to ensure analytics are accurate and trusted across the org.
• Automation & Tooling: Identify manual, repetitive analytical workflows and automate them — building reusable tools and queries that scale the team's analytical capacity.
• Cross-Functional Partnership: Partner with Product, Supply Chain Managers, Finance, and Operations to understand analytical needs, prioritize requests, and deliver data solutions that inform cost optimization decisions.

Requirements & Tech Stack

• Experience analyzing and interpreting data with Redshift, Oracle, NoSQL, etc.
• Experience with SQL, ETL, or Oracle.
• Experience processing large, multi-dimensional datasets from multiple sources.
• Experience performing statistical analysis.
• Experience developing automated reporting.
• Experience using SQL to pull data from a database or data warehouse.
• Scripting experience (Python) to process data for modeling.
• 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.

Qualifications

• Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, Statistics, Economics, or a related field.
• Minimum of 1 year of experience in the job offered or a related occupation (based on interpretation of multiple "1+ years" qualifications in the raw text).
• Minimum of 3 years of experience in the job offered or a related occupation (based on interpretation of multiple "3+ years" qualifications in the raw text).

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.
• Usage of generative AI tools to enhance workflow efficiency, with a willingness to learn effective prompting and evaluation practices.
• Ability to recognize opportunities where generative AI could enhance products, workflows, or customer experiences.