Senior Engineering Manager

Senior Engineering Manager – Data & AI is responsible for leading high-performing engineering teams that deliver scalable data products, AI/ML capabilities, analytics foundations, and reliable platforms across Maersk. The role combines people leadership, stakeholder partnership, product delivery, data architecture, and operational excellence to translate business priorities into secure, resilient, production-grade technology solutions. It requires strong experience in data engineering, cloud platforms, AI-assisted development, engineering governance, and end-to-end delivery from prototype to production adoption. JL5 Senior Engineering Manager – Data & AI team WHO WE ARE At Maersk, we are redefining global logistics through data, platform engineering, and AI-driven innovation. As part of this journey, we are building scalable platforms and intelligent systems that enable faster decision-making, operational efficiency, and seamless integration across the enterprise. The Position As a Senior Engineering Manager in the Data & AI team, you will lead engineering teams responsible for delivering scalable data products, AI-enabled capabilities, analytical foundations, and reliable platforms that power critical business decision-making across Maersk. You will be accountable for building and engaging a high-performing team, shaping pragmatic data architecture, partnering with product and business stakeholders, and ensuring successful delivery of data, analytics, and AI/ML solutions. The role requires a hands-on, forward-looking leader who can connect business problems to technical outcomes, guide teams from quick prototypes to production-grade products, and leverage modern AI coding agents to accelerate experimentation, solution design, and engineering delivery. Key Responsibilities Team Leadership, Engagement & Engineering Management • Build, lead, coach, and engage high-performing engineering teams across data engineering, AI/ML engineering, analytics enablement, and platform delivery • Create clarity on priorities, ownership, ways of working, delivery commitments, and engineering expectations across the team • Support resource and demand planning, capability building, knowledge sharing, and continuous development of team members Stakeholder Management, Requirements & Business Partnership • Collaborate with product owners, business stakeholders, leadership, architecture, data science, and platform teams to understand business priorities and translate them into executable technology outcomes • Act as a trusted partner and voice of customer success by ensuring solutions address real business needs, adoption, usability, operational reliability, and measurable value • Drive clarity on requirements, trade-offs, timelines, risks, dependencies, and priorities across cross-functional stakeholders Product Delivery, Prototyping & Innovation • Lead end-to-end delivery of data products, analytics capabilities, AI/ML solutions, dashboards, and platform capabilities from discovery through production adoption • Guide teams to quickly validate ideas through prototypes, proofs of concept, and iterative delivery before scaling validated solutions • Use modern AI coding agents and engineering automation to accelerate prototyping, solution exploration, developer productivity, and delivery effectiveness Data Architecture, AI/ML Engineering & Platform Excellence • Shape scalable, governed, secure, and reusable data architecture across data engineering, analytics, visualization, AI/ML, and GenAI use cases • Provide technical leadership on data modelling, data pipelines, orchestration, cloud data platforms, integration patterns, and production-grade AI/ML engineering practices • Ensure solutions are horizontally scalable, resilient, observable, secure, and aligned with enterprise architecture and information security expectations Cross-Functional Delivery & Architecture Governance • Partner across Product, Analytics, Data Science, AI, Platform, Architecture, Security, and business teams to deliver pragmatic end-to-end solutions • Translate business requirements, user feedback, and problem statements into delivery roadmaps, technical direction, architectural decisions, and implementation plans • Drive informed decision-making by balancing speed, quality, resilience, scalability, cost, security, and business value • Support integrations across cloud, enterprise applications, and data ecosystems Operational Excellence • Champion engineering excellence, DevOps/DataOps practices, Site Reliability principles, observability, monitoring, incident management, and continuous improvement • Ensure team-owned products and platforms are reliable, scalable, secure, supportable, and production-ready • Drive automation, operational readiness, support models, and continuous learning across data, analytics, and AI/ML solutions Our Ideal Candidate • Experienced engineering leader with proven ability to build, manage, coach, and engage high-performing teams • Strong stakeholder management skills with the ability to engage business, product, leadership, architecture, and platform stakeholders • Strong product delivery mindset with experience taking ideas from discovery and prototype through production adoption and continuous improvement • Hands-on technical leader with solid understanding of data engineering, SQL, data modelling, data pipelines, orchestration, cloud platforms, visualization, and AI/ML engineering concepts • Comfortable using AI coding agents and modern engineering tools to rapidly prototype solutions, test ideas, and accelerate delivery • Skilled communicator and collaborator with strong ownership, problem-solving ability, and passion for continuous learning and innovation Required Skills/Experience • MS or BS in a Computer Science or Engineering discipline. • More than 10 years of technology experience, including significant experience leading engineering teams and delivering data, analytics, platform, or AI/ML products • Experience managing team priorities, delivery commitments, stakeholder expectations, resource planning, and capability development • Strong understanding of data architecture, SQL, data modelling, data pipelines, orchestration, cloud data platforms, visualization enablement, and AI/ML engineering practices • Experience with cloud-ready, scalable, resilient, secure, observable, and high-availability platforms and products • Hands-on familiarity with AI-assisted development, AI coding agents, or modern engineering automation for rapid prototyping and productivity improvement • Experience with DevOps, DataOps, Site Reliability principles, CI/CD, monitoring, observability, information security, troubleshooting, and incident support 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. We are happy to support your need for any adjustments during the application and hiring process. If you need special assistance or an accommodation to use our website, apply for a position, or to perform a job, please contact us by emailing  accommodationrequests@maersk.com . CORE SKILLS AI & Machine Learning: The field of artificial intelligence (AI) involves creating systems that can perform tasks that typically require human intelligence. Machine learning (ML) is a subset of AI that uses algorithms to learn from and make predictions based on data Proficiency Level: Advanced Data Product Development: The creation of products that leverage data to provide insights, predictions, or other value to users. Proficiency Level: Advanced Data Engineering: The practice of designing and building systems for collecting, storing, and analyzing data at scale. Proficiency Level: Advanced Data Architecture: The design and structure of data systems, ensuring that data is stored, managed, and utilized efficiently Proficiency Level: Advanced SPECIALIZED SKILLS Cloud Computing: A field of AI that trains computers to interpret and make decisions based on visual data from the world, such as images and videos Data Governance and Compliance: The management of data availability, usability, integrity, and security in an organization, based on internal data standards and policies. Ensuring that an organization adheres to external regulations and internal policies, managing risk, and maintaining ethical standards Data Security: Protecting data from unauthorized access, use, disclosure, disruption, modification, or destruction. Business Intelligence: Protecting data from unauthorized access, use, disclosure, disruption, modification, or destruction. Customer Insights: The analysis of customer data and feedback to understand their behaviors and preferences, helping businesses make informed decisions Cybersecurity Awareness: Understanding and implementing practices to protect systems, networks, and programs from digital attacks Definition of Proficiency Levels: Foundational: This is the entry level of the skill, typically expected when starting a new role or working with the skill for the first time. You rely on strong manager support, coaching, and training as you build the capability to progress to higher proficiency levels. Proficient: This is the level at which you are considered effective in the skill. You demonstrate more than just functional competence—you begin to have a noticeable impact in your role by applying the skill consistently and meaningfully. You require only minimal support, coaching, or training to apply the skill successfully. Advanced: This is the level where you move beyond meeting expectations to actively leading, influencing, and delivering considerable impact across the wider business. You are seen as a role model, demonstrate the skill independently, and require little to no manager support.