Business Intelligence Engineer, Amazon Intermodal
Amazon · Atlanta, Georgia, USA ·
- Category
- Data analytics
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 job responsibilities
Data Pipeline & Infrastructure 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.
A day in the life
You'll start each morning validating overnight data pipeline runs and checking dashboards for anomalies or data quality issues. From there, you'll dig into an analytical deep dive — querying large datasets to size a cost savings opportunity, investigating a network anomaly, or building a new metric requested by the team. You'll partner with Product Managers and Supply Chain Managers to translate business questions into data solutions, and collaborate with Finance to align on cost baselines and definitions. You'll spend time building — writing ETL code, refining data models, or automating a manual report. When you uncover an insight, you'll package it into a clear visualization or write-up for stakeholders and leadership.
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 job responsibilities
Data Pipeline & Infrastructure 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.
A day in the life
You'll start each morning validating overnight data pipeline runs and checking dashboards for anomalies or data quality issues. From there, you'll dig into an analytical deep dive — querying large datasets to size a cost savings opportunity, investigating a network anomaly, or building a new metric requested by the team. You'll partner with Product Managers and Supply Chain Managers to translate business questions into data solutions, and collaborate with Finance to align on cost baselines and definitions. You'll spend time building — writing ETL code, refining data models, or automating a manual report. When you uncover an insight, you'll package it into a clear visualization or write-up for stakeholders and leadership.