Engineering Director

Maersk Mexico; Bengaluru; On-site; INR 450,000 - 750,000; Full time

Job summary

Maersk Mexico is seeking an Engineering Director for Data & AI to lead modernization of data engineering, analytics, reporting, and AI-enabled product capabilities across the SAP landscape. You will shape strategy, architecture, and execution to deliver scalable, governed, self-service data and AI platforms.

You will collaborate with product, platform, security, governance, and business teams, driving efficient data products, improved decision quality, and cost optimization while keeping current

Responsibilities

• Data Engineering Modernization: define and lead modernization across ingestion, transformation, orchestration, quality, lineage, and consumption layers.
• Self-Service Data & Reporting Enablement: create governed data layers and enable self-service discovery, reporting, and reusable metrics.
• AI-Based Engineering & Data Product Outcomes: drive AI-based engineering, development, and reusable data products with measurable value.
• Keeping Existing Priorities on Track: maintain current data/analytics commitments while introducing modernization with clear ownership and milestones.
• Architecture, Governance & Platform Alignment: align data/analytics/AI architecture with enterprise platforms, security, and governance.

Job description

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, 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, i