Business Intelligence Engineer, Supply Chain Business Insights

Help shape the data foundation behind one of Amazon's fastest-growing supply chain networks — and put AI in the hands of the people who move millions of packages across Mexico every day. This is your chance to build the pipelines, analytics, and cloud-native solutions that turn raw data into trusted, accurate intelligence for an organization at the forefront of GenAI adoption.

As a Business Intelligence Engineer on the MX Supply Chain BI team, you will design, build, and maintain scalable data pipelines, reporting solutions, and analytical tools that power decisions across Mexico's Supply Chain — from network planning and capacity to last mile operations. You will work with large-scale datasets on AWS, write production-grade Python code, deploy cloud-based solutions, and partner with stakeholders across 10 functional areas to deliver insights that drive operational performance. You will contribute to the team's AI-ready data lakehouse platform and help embed GenAI and ML capabilities into the analytics portfolio.

Key job responsibilities
Design, develop, and maintain scalable ETL/big-data pipelines using AWS services following medallion-architecture best practices

Write production-grade Python code for data transformation, automation, statistical analysis, and integration with AI/ML workflows

Build high quality datasets with robust metadata for agents to consume and give insights to our stakeholders.

Deploy and manage cloud-native data solutions on AWS through infrastructure-as-code based deployments.

Collaborate with stakeholders across Supply Chain functional areas (Topology, S&OP, Network Planning, Middle Mile, Last Mile, Sort Centers, Operations) to translate business questions into analytical solutions

Contribute to the team's AI-ready data lakehouse platform (Apache Iceberg on serverless AWS), ensuring data quality, governance, and single-source-of-truth standards

Write and optimize complex queries on SQL, Spark SQL and PySpark across large-scale datasets to support reporting, ad-hoc analysis, and metric development

Establish and follow engineering best practices including code reviews, testing, monitoring, documentation, and logging best practices.

Proactively identify opportunities to improve data availability, reduce time-to-insight, and automate manual reporting processes

Support the team's GenAI/ML initiatives by preparing governed, high-quality datasets and contributing to data products that enable agentic and self-service AI workflows

A day in the life
You start your morning reviewing pipeline health dashboards and resolving any data quality alerts from overnight ETL runs. By mid-morning, you're deep in Python — building a new data transformation module. After a quick stand-up with your peers, you jump into a working session with a Last Mile stakeholder who needs a new metric added to their operational dashboard; you scope the requirement, write the code and push for your peers to review before lunch.

You wrap up the day by prototyping a data integration that will expose a new dataset to the team's GenAI agent through the MCP — a small step that will let operations leaders ask natural-language questions about network speed and compliance. No two days are the same, but every day your work makes Supply Chain decisions faster, smarter, and more trusted.

About the team

MX Supply Chain Business Insights owns the data, insights, and analytics that power how Amazon's Mexico Supply Chain organization understands its business, measures its customer experience, and makes decisions. The team serves 10 functional areas with growing cross-LATAM collaboration with Brazil.

Our team works together to deliver the data infrastructure, pipelines, semantic layers, and reporting that underpin MX Supply Chain's most critical business metrics, while also developing full stack applications to gather and process data. We are at an inflection point: as we embed GenAI across the portfolio and scale an AI-ready data lakehouse across LATAM, the decisions our stakeholders make depend on data they can trust, access quickly, and act on confidently.