Lead Engineer - Data & AI

Job Description A.P.

Moller - Maersk is an integrated container logistics company and member of the A.P.

Moller Group.

Connecting and simplifying trade to help our customers grow and thrive.

With a dedicated team of over 80,000, operating in 130 countries; we go all the way to enable global trade for a growing world.

We leverage cutting-edge technology to optimize operations, enhance customer experience, and drive business growth.

We are seeking a Lead Engineer - Data & AI to join our team and play a pivotal role.

About the role Maersk moves a significant share of the world's containerized trade and is a major player in the logistics and services space.

The data is large, complex and consequential.

This role offers real architectural ownership of a platform that matters, with the freedom to build it yourself, within a team that treats AI tooling as a serious part of engineering practice.

This is a senior individual contributor role for an engineer who designs and builds the most critical parts of our systems.

You will partner in setting the architecture for our data and AI platform, decide how the major pieces fit together, and then write and deploy the code that proves the design works.

Design documents and diagrams are part of the job, but they are the beginning of the work rather than the output.

The engineers who do well in this role are the ones whose designs are trusted because they have shipped the hard parts themselves.

The scope spans the full stack of a data and AI platform: storage and table formats, pipelines, services, applications, and the agentic systems built on top.

You will make the calls on open standards and interoperability, on build against buy, and on how we sequence a migration without disrupting what is already running.

You will also set how this organization uses agentic coding, which at this level means building the tooling and the standards that other engineers work within.

The role carries no direct reports.

Influence comes from the quality of your designs, the code you ship, and the engineers who get better by working alongside you.

Main Responsibilities: Own the architecture for major platform components and data products, from storage layer through to the application and agent layer.

Define target architectures and the migration paths to reach them, with attention to open standards, interoperability and long-term flexibility.

Write and deploy production code.

You will personally build the difficult components and the reference implementations others extend.

Make build against buy decisions with clear reasoning on cost, operational load and dependency risk.

Design the platform foundations that other teams rely on: data contracts, lineage, schema evolution, access control and cost management.

Set the agentic engineering practice across the group, including internal tooling, MCP servers, reusable skills, evaluation harnesses and the standards for reviewing generated code.

Take on the technical problems that are genuinely hard or genuinely ambiguous, and bring them to a working resolution.

Raise the engineering bar through design review, code review and mentoring.

Partner with Product, Business stakeholders and Engineering Managers on sequencing, trade-offs and technical risk.

Core Skills: Programming: Writing code to manipulate, analyze, and visualize data, often using languages like Python, R, and SQL.

AI & Machine Learning: Creating systems that can perform tasks that typically require human intelligence.

Using Machine learning (ML), a subset of AI that uses algorithms to learn from and make predictions based on data Data Analysis: Inspecting, cleansing, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making Machine Learning Pipelines: Using automated workflows that manage the end-to-end process of training and deploying machine learning models.

Model Deployment: Making a trained machine learning model available for use in production environments.

Required skills: Architecture and system design Has designed and delivered systems that multiple teams build on, and has lived with the consequences of those decisions.

Strong command of distributed systems fundamentals, including partitioning, consistency models, delivery guarantees, backpressure and schema evolution.

Designs for the non-functional requirements from the start: reliability, recovery, latency, cost, security and operability.

Has migrated live systems to a new architecture while keeping them running.

Writes design documents that another engineer can build from without further explanation.

Data engineering Platform-level design of table formats and catalogs, covering Iceberg, Delta Lake or Hudi, along with partitioning, clustering, compaction and file layout strategy.

Both batch and streaming architectures, including change data capture and event-driven ingestion.

Transformation frameworks at scale, such as dbt, together with semantic layer design.

Data modelling depth sufficient to set standards that other teams follow.

Performance and cost tuning at platform level, including query engines, storage layout and compute sizing.

Multi-tenant governance, data quality frameworks and lineage.

Software engineering Deep expertise in at least one language and working fluency in others.

Service and API design, including versioning, backwards compatibility and contract management across teams.

Observability as a design concern: instrumentation, tracing, structured logging and meaningful alerting.

Infrastructure as code using Terraform, Bicep or equivalent, and container orchestration with Kubernetes.

Security practice covering authentication, authorisation, secrets management and data protection.

Front-end capability sufficient to build a usable interface when a product needs one.

AI and agentic engineering Expert-level use of agentic coding tools.

You work through agents for a large share of your output and can explain your harness, your context strategy,