Sr BIE Manager, Supply Chain, Supply Chain, Mexico

Sr BIE Manager, Supply Chain, Supply Chain, Mexico

Lead the data and business insights team along with the AI strategy for Mexico's Supply Chain organization, owning a portfolio of BI and data products that power decisions across 10 functional areas, from network topology and capacity planning to last mile operations and cross-LATAM AI enablement.

As a Sr. Manager of Business Intelligence Engineering, your first role is to lead a group of 11 Business Intelligence Engineers: teach, coach, and develop them as your first priority. You will build a high-performing team that is stronger because of your presence but does not require your presence to be successful. You will attract and retain top BI talent, decide staffing and skill mix, drive growth into higher-technical-complexity work, and keep the team motivated through recognition, codified onboarding, and work-life harmony across a distributed team.

After that, you will own the AI strategy and data systems for the organization, leading the data infrastructure, pipelines, and reporting that power a portfolio of BI and data products across Mexico's Supply Chain. You will increase data availability and efficiency, improve data confidence and reliability, and scale existing systems into AI-ready platforms while building net-new infrastructure to support the needs of a rapidly evolving Supply Chain business. You will own end-to-end analytical solutions that are highly available, scalable, stable, secure, and cost-effective.

This is a unique opportunity to build the AI-ready data foundation for one of Amazon's fastest-growing markets, lead a group of 11 Business Intelligence Engineers, and define how GenAI and ML transform supply chain decision-making at scale.

MX Supply Chain Business Intelligence & AI 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 including Topology, S&OP, Network Planning, Middle Mile, Last Mile (OTR/UTR), Sort Centers, External Fulfillment, and FC Operations, with growing cross-LATAM collaboration with Brazil. The data that powers our decisions, from network capacity and speed to compliance, cost, and operational performance, must be available, efficient, and trusted.

Our team of Business Intelligence Engineers works together to deliver the data infrastructure, pipelines, semantic layers, and reporting that underpin MX Supply Chain's most critical business metrics. We are at an inflection point: as we embed GenAI across the portfolio and scale an AI-ready data lakehouse (Apache Iceberg on serverless AWS) across LATAM, the decisions our stakeholders make depend on data they can trust, access quickly, and act on confidently.

Key job responsibilities

• Drive team growth through mentoring, coaching, career development, BIE tech assessments, and lateral moves into roles of higher technical complexity
• Own the AI-ready data foundation that powers MX Supply Chain's GenAI/ML initiatives, including the data lakehouse platform and Model Context Protocol (MCP) servers that expose trusted, governed datasets to agentic and self-service AI workflows
• Delivery of robust, scalable data solutions supporting MX Supply Chain's data infrastructure across all functional areas, mile types, and geos
• Leadership in data governance, single-source-of-truth definitions, quality standards, metric accountability, and SLA enforcement across the MX Supply Chain data ecosystem
• Collaborate with central teams to deploy AI solutions and align strategies and resources, negotiating coexistence and shared metric definitions while preserving local execution speed
• Own technical delivery and team leadership for development and maintenance of scalable ETL/big-data pipelines, medallion-architecture data lakes, semantic layers, and dashboard data models
• Collaborate with Product Management, BI teams, and stakeholders to prioritize work aligned with MX Supply Chain business goals
• Establish best practices for data engineering, including code reviews, testing, monitoring, naming conventions, alarms, and documentation
• Reduce time-to-insight for product, leadership, and partner teams by increasing data availability and efficiency

About the team

The team is responsible for the development of business-critical BI and analytics solutions: datasets, real-time capacity and speed analytics, ML-based planning models, network speed analytics, automated compliance platforms, and an AI-ready data lakehouse serving as the LATAM data platform built on Apache Iceberg with Infrastructure as Code.

The incumbent sets the organization's BI architecture where the strategy is not yet defined, operating with complete independence in ambiguous problem spaces to determine not only how to build, but which business questions the organization should be asking of its data.

Basic Qualifications

• 6+ years of business intelligence and analytics experience
• 6+ years of delivering results managing a business intelligence or analytics team, including employee development and performance management experience
• Experience with data visualization using Tableau, Quicksight, or similar tools
• Experience using Python or R for data analysis or statistical tools such as SAS
• Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, or a related field
• Experience with AWS products and services
• Experience in machine learning, data mining, information retrieval, statistics or natural language processing, or experience in computer architecture
• 5+ years of SQL experience
• 5+ Experience in ETL management/data pipeline

Preferred Qualifications

• Experience working directly with business stakeholders to translate between data and business needs
• Experience managing, analyzing and communicating results to senior management
• Experience in creating process improvements with automation and analysis, or experience communicating results to senior leadership
• Experience developing, deploying and managing AI products at scale
• 7+ years of developing automated reporting experience
• Knowledge of concepts like system architecture, optimization, system dynamics, system analysis, statistical analysis, reliability analysis, electronic system design, and decision making

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