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Amazon’s Intermodal Network Optimization team seeks a Business Intelligence Engineer to build data foundations and tooling that power cost optimization across the intermodal network. You’ll own end‑to‑end analytics from data modeling to visualization and insights, enabling fast, data‑driven decisions.
You’ll work with product managers, supply chain leads, and finance to surface cost reduction opportunities, drive efficiency, and scale automated reporting across large datasets using SQL, Python,
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.
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.
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