Senior Engineering Manager, AI & Data Platform

About the role

Maersk is building enterprise-scale data and AI platforms to power intelligent, integrated supply chain solutions. It is looking for a Senior Engineering Manager for its AI & Data Platform, who will lead one or more engineering teams delivering scalable data platforms and AI-enabled capabilities. The role covers data engineering, platform development and agentic AI enablement, with accountability for delivery, quality, team capability and platform reliability.

Key responsibilities

• Lead delivery of data platforms, data products and AI-enabled applications, and enable agentic AI adoption so data pipelines support AI-driven workflows
• Enable democratised data access through governed, queryable data layers decoupled from operational systems, and drive self-service reporting and dashboards
• Ensure alignment with enterprise architecture, platform and cloud strategies, and promote reusable platform components and engineering standards
• Support integration across SAP and non-SAP ecosystems
• Lead and develop engineering teams, own delivery outcomes and quality standards, and drive Agile execution and prioritisation
• Ensure scalability, reliability and performance at global scale, with observability, automation and DevOps practices
• Optimise platform cost through efficient architecture and cloud usage, and adopt shared platforms and reusable services
• Collaborate across Product, SAP, Infrastructure and Data teams and influence decisions affecting enterprise data and AI platforms

Required profile

• MS or BS in Computer Science or another science/engineering discipline
• 18+ years of experience in technology leadership roles
• Experience leading teams as Engineering Manager, Senior Engineering Manager or Director
• Proven track record delivering scalable data and platform solutions in enterprise environments
• Exposure to AI/ML or agentic AI use cases
• Strong stakeholder management and execution leadership

Required skills

• Data platforms, pipelines and analytics ecosystems
• Cloud platforms (Azure preferred) and modern DevOps practices
• Understanding of AI/ML platforms and data requirements for AI use cases