AI-Driven Supply Chain Analytics Engineer

Dana Incorporated; Ciudad de México; On-site; MXN 700,000 - 900,000; Full time

Job summary

Dana Incorporated is seeking an Analytics Engineer to build and maintain data foundations powering analytics, AI-driven insights, and reporting across its global supply chain. You will transform raw operational data into reliable, well‑modeled datasets to improve forecast accuracy and optimize material flow.

You will design scalable data pipelines, enforce data governance, and develop AI‑driven forecasting and KPIs to support cost reductions and more informed decisions across plants and

Qualifications

• Bachelor’s degree required; MBA or CPIM/CSCP preferred.
• Minimum of 5 years of experience in analytics, supply chain, or operations analytics.
• Power BI experience is required; SAP MM/PP familiarity is a plus.

Responsibilities

• Design, build, and maintain SQL‑based data models and transformations using dbt.
• Develop scalable data pipelines integrating SAP, logistics systems, planning tools, and external sources.
• Implement data quality tests, documentation, and version control practices.
• Ensure governance and semantic consistency across data domains (inventory, demand, supply, logistics, procurement).
• Build and maintain AI‑driven forecasting, inventory optimization, and supply‑risk models.
• Analyze inventory levels, lead times, and working capital to optimize costs.
• Develop real time dashboards and reports to improve visibility and decision making.

Skills

SQL

dbt

Data modelling

AI forecasting

Stakeholder comms

Education

Bachelor’s degree

MBA or CPIM/CSCP preferred

Tools

PowerBi

SAP MM/PP

Job description

Dana Incorporated is seeking an Analytics Engineer to build and maintain data foundations powering analytics, AI-driven insights, and reporting across its global supply chain. You will transform raw operational data into reliable, well‑modeled datasets to improve forecast accuracy and optimize material flow.

You will design scalable data pipelines, enforce data governance, and develop AI‑driven forecasting and KPIs to support cost reductions and more informed decisions across plants and