Senior Engineering Manager

A.P. Moller Maersk; Pune District, Bengaluru; On-site; INR 4,000,000 - 8,000,000; Full time

Job summary

A.P. Moller Maersk is looking for a Senior Engineering Manager - Data AI to lead high‑performing teams delivering scalable data products, AI-enabled capabilities, and reliable platforms that power critical business decisions.

You will shape data architecture, partner with product and business stakeholders, and drive end‑to‑end delivery from prototype to production adoption. You will leverage AI coding agents to accelerate experimentation, solution design, and engineering delivery while ensuring

Qualifications

• 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

Responsibilities

• 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
• 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
• 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
• Shape scalable, governed, secure 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

Skills

Data engineering

SQL

Data modelling

Data pipelines

Orchestration

Cloud platforms

AI/ML engineering

DevOps/DataOps

Site Reliability

Leadership

Education

MS or BS in Computer Science or Engineering

Tools

Cloud platforms

CI/CD

Observability tools

Job description

Job Summary

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

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