PUNE · FULLTIME
Senior Engineering Manager
Maersk Global Service Centres
Pune · onsite · Posted 11d ago
Sourced from
Undisclosed10–15 yrsfulltimePune
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Section · 01
About this role
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
- 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, 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
- 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
- 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
- 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
- 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
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Section · 02
Skills
Automationcloud platformsbsCapability BuildingBusiness Requirementscustomer successcontinuous developmentAnalyticsCloud DatacloudAlgorithmsArchitecturecomplianceBusiness IntelligenceArtificial Intelligence
Section · Company
About Maersk
Maersk Global Service Centres
Logistics
30k+
employees
1904
122 years old
Bengaluru/Bangalore, Karnataka
India
₹10.2L PA avg
Avg at Maersk
About
A.P. Moller - Maersk is an integrated transport and logistics company; going all the way, together, for our customers and society.
Industries
LogisticsPorts & Shipping
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