Engineering Director

Overview

Lead the modernization of Maersk's data engineering, analytics, reporting and AI-enabled product capabilities, moving from fragmented, tool-heavy, centrally dependent models to scalable, governed, self-service capabilities.

What you'll do

• Define and lead the modernization of data engineering 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.
• Establish practices for data reliability, observability, performance, supportability and operational resilience.
• 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 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 and fit-for-purpose lower-cost technology choices.
• Drive AI-based engineering, including AI-assisted development, intelligent data quality, automated insight generation, knowledge retrieval and agentic workflows where they create measurable value.
• Build data and AI products measured through adoption, operational improvement, cost reduction, better decisions or productivity gains.
• Link data, analytics, reporting and AI investments to clear value cases, usage measures and business benefit realization.
• Promote responsible, secure and governed AI adoption across engineering teams.
• Maintain delivery discipline across current data engineering, analytics, reporting and platform commitments while introducing modernization in a controlled way.
• Identify and remove execution blockers across teams, vendors, platforms and governance forums.
• Partner with enterprise architecture, platform, security, governance and product teams to define target-state data, analytics, reporting and AI architecture.
• 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.
• Work across Product, SAP, Platform, Infrastructure, Security, AI, Data Governance and business teams beyond direct reporting lines.
• Embed modern engineering practices including DevOps, automation, CI/CD, testing, observability and AI-assisted software delivery.
• 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 and a continuous improvement mindset.

What you'll need

• 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.
• Experience leading data engineering, analytics, reporting, AI or data platform teams at scale.
• Track record of modernizing fragmented or tool-heavy data landscapes into scalable, governed and cost-efficient engineering-led capabilities.
• Experience enabling self-service data access and self-service reporting while maintaining governance, quality, security, privacy and compliance.
• Practical understanding of AI/ML, GenAI and 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.
• Ability to influence and deliver across teams outside direct span of control while keeping priorities on track.
• Experience aligning senior stakeholders around data, AI, reporting, cost, risk and platform priorities.

Nice to have

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

Details

• Location: Bangalore, India.