Engineering Director

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 where needed, and aligned to enterprise controls.

Keeping Existing Priorities on Track

• Maintain delivery discipline across current data engineering, analytics, reporting, and platform commitments while introducing modernization in a controlled, outcome-focused way.

• Protect continuity of in-flight priorities by ensuring clear ownership, transparent sequencing, strong stakeholder communication, and measurable delivery milestones.

• Balance modernization, cost optimization, AI adoption, and business-as-usual reliability so existing business-critical reporting and data services remain stable.

• Identify and remove execution blockers across teams, vendors, platforms, and governance forums to keep priority outcomes moving at pace.

Architecture, Governance & Platform Alignment

• Partner with enterprise architecture, platform, security, governance, and product teams to define target-state data, analytics, reporting, and AI architecture.

• Ensure data and AI platforms are aligned with broader cloud, integration, SAP, security, infrastructure, and enterprise architecture strategies.

• Define guardrails for data governance, AI governance, data quality, privacy, access management, resiliency, cost optimization, and operational controls.

• Guide build-vs-buy decisions with a pragmatic view of cost, speed, flexibility, engineering leverage, regulatory requirements, and long-term enterprise value.

Cross-Organizational Influence

• Work collaboratively across Product, SAP, Platform, Infrastructure, Security, AI, Data Governance, and business teams beyond direct reporting lines.

• Influence enterprise decisions impacting data foundations, reporting modernization, AI adoption, platform investment, and cost optimization.

• Drive alignment across diverse stakeholders with competing priorities, ensuring the function remains focused on enterprise outcomes rather than local optimization.

Engineering Excellence & Delivery

• Embed modern engineering practices, including DevOps, automation, CI/CD, testing, observability, reusable engineering patterns, and AI-assisted software delivery across data and AI platforms.

• Ensure delivery of scalable, secure, high-quality data platforms, reporting services, AI-enabled capabilities, and outcome-based data products.

• Establish performance, reliability, quality, adoption, value realization, and cost optimization metrics with strong governance controls.

• Build and scale high-performing teams with strong ownership, technical depth, delivery discipline, customer orientation, and continuous improvement mindset.

Our Ideal Candidate

You are a strategic and execution-focused leader who has successfully driven large-scale data, analytics, reporting, AI, or platform transformations in complex enterprise environments.

• Proven experience leading data engineering, analytics, reporting, AI, or data platform teams at scale.

• Demonstrated track record of modernizing fragmented or tool-heavy data landscapes into scalable, governed, and cost-efficient engineering-led capabilities.

• Strong understanding of enterprise data platforms, semantic layers, reporting ecosystems, AI platforms, cloud platforms, and distributed systems.

• Experience enabling self-service data access and self-service reporting while maintaining governance, quality, security, privacy, and compliance.

• Strong practical understanding of AI/ML, GenAI, agentic AI use cases, and the data foundations required to deliver them reliably at enterprise scale.

• Ability to make pragmatic build-vs-buy choices and reduce dependency on costly enterprise software where fit-for-purpose alternatives can deliver better outcomes.

• Ability to influence and deliver across teams outside direct span of control while keeping current priorities and commitments on track.

• Strong leadership presence with experience aligning senior stakeholders around data, AI, reporting, cost, risk, and platform priorities.

• Customer- and value-driven mindset with strong ownership, accountability, and focus on measurable business impact.

• Ability to coach and develop engineers, uplifting technical capability and establishing a strong data and AI engineering culture.

Required Skills/Experience

• MS or BS in Computer Science, Data Engineering, Information Systems, AI/ML, or a science/engineering discipline.

• 18+ years of experience in technology leadership roles, with significant experience in data engineering, analytics, reporting, platform engineering, or AI-enabled products.

• Proven experience driving large-scale transformation and modernization programs across data, analytics, reporting, AI, or platform domains.

• Strong experience with modern data platforms, data pipelines, ETL/ELT, APIs, semantic layers, data quality, metadata, governance, and reporting ecosystems.

• Experience with cloud platforms, preferably Azure, and modern DevOps / platform engineering practices.

• Experience designing scalable architectures that support analytics, self-service reporting, AI/ML, GenAI, and decision intelligence use cases.

• Demonstrated ability to lead cross-functional initiatives across distributed teams and influence senior stakeholders.

• Strong business acumen and ability to balance cost, risk, agility, user experience, reliability, and long-term enterprise value.

Maersk is committed to a diverse and inclusive workplace, and we embrace different styles of thinking. Maersk is an equal opportunities employer and welcomes applicants without regard to race, colour, gender, sex, age, religion, creed, national origin, ancestry, citizenship, marital status, sexual orientation, physical or mental disability, medical condition, pregnancy or parental leave, veteran status, gender identity, genetic information, or any other characteristic protected by applicable law. We will consider qualified applicants with criminal histories in a manner consistent with all legal requirements.

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