Engineering Director – Data & AI
WHO WE ARE
At Maersk, we are on a transformative journey to become the Global Integrator of container logistics, connecting and simplifying our customers' supply chains through technology, data, and innovation. Our SAP landscape is a critical backbone of this vision, and we are accelerating a shift towards modern, scalable, and cost-efficient in-house engineering capabilities.
The Position
As Engineering Director for Data & AI, you will lead a high-impact organization responsible for modernizing enterprise data engineering, analytics, reporting, and AI-enabled product capabilities. You will keep the current priorities on track while accelerating the shift from fragmented, tool-heavy, and centrally dependent models to scalable, governed, self-service capabilities that reduce overhead and improve business outcomes.
You will own the strategy, architecture alignment, and execution of modern data foundations, self-service data query and reporting experiences, and AI-based engineering practices. The role requires deep collaboration across product, platform, SAP, enterprise architecture, security, data governance, and business teams—often beyond direct reporting lines—to influence outcomes at scale.
You will play a critical leadership role in reducing dependency on costly enterprise software, enabling engineering-led innovation, and building outcome-based data and AI products that improve decision quality, operational efficiency, customer experience, and user productivity.
Key Responsibilities
Data Engineering Modernization
Define and lead the modernization of data engineering capabilities across ingestion, transformation, orchestration, quality, lineage, and consumption layers.
Reduce operational overhead through automation, reusable data patterns, shared engineering standards, and simplified platform architecture.
Drive the transition from project-by-project data delivery to scalable, product-oriented data foundations that support analytics, reporting, AI, and decision intelligence use cases.
Establish clear engineering practices for data reliability, observability, performance, supportability, and operational resilience across critical data pipelines and products.
Self-Service Data & Reporting Enablement
Create governed, queryable data layers that enable business and product teams to access trusted data without dependency on costly enterprise software or central reporting bottlenecks.
Enable intuitive self-service data discovery, query, reporting, and reuse through standard semantic layers, reusable metrics, data contracts, metadata, and access governance.
Modernize reporting capabilities to deliver scalable, self-service dashboards and insights with lower total cost of ownership.
Rationalize reporting tools, dashboards, and data marts by promoting shared metrics, standard data products, reusable templates, and fit-for-purpose lower-cost technology choices where appropriate.
Ensure reporting outputs are reliable, timely, consistent, secure, and aligned to business-critical decision-making needs.
AI-Based Engineering & Data Product Outcomes
Drive a strong focus on AI-based engineering, including AI-assisted development, intelligent data quality, automated insight generation, knowledge retrieval, and agentic workflows where they create measurable value.
Build impactful data and AI products that solve priority business problems and are measured through adoption, operational improvement, cost reduction, better decisions, or productivity gains.
Ensure data products are designed for reuse, discoverability, interoperability, and integration with AI platforms, copilots, agents, and decision intelligence solutions.
Establish an outcome discipline where data, analytics, reporting, and AI investments are linked to clear value cases, usage measures, and business benefit realization.
Promote responsible, secure, and governed AI adoption across engineering teams, ensuring AI solutions are reliable, explainable…