Senior Data Engineer

About the role

Maersk is looking for a Senior Data Engineer within its Data & AI team in Copenhagen. You will design, build and operate scalable data products, pipelines and analytical foundations that power business insights and AI-enabled decisions. The role spans data engineering, analytics enablement, visualization and AI/ML engineering, working with business stakeholders and product teams to turn their needs into practical data solutions.

Key responsibilities

• Design, build and optimize batch and streaming data pipelines, and orchestration workflows for ingestion, transformation, quality checks and downstream consumption
• Create well-modelled, trusted datasets and data contracts that support reporting, visualization, analytics and AI/ML, and enable self-service data access
• Deliver datasets, dashboards and semantic layers with analytics and product teams, ensuring performance, usability and clear data lineage
• Build pipelines and feature-ready datasets for machine learning, AI and GenAI, and collaborate with data scientists to productionize models
• Work with Product, Analytics, Platform, Data Science and AI teams to deliver end-to-end data solutions, including prototypes and technical designs
• Keep data solutions reliable, scalable and secure, and monitor and improve pipeline performance and data quality

Required profile

• MS or BS in Computer Science or a science/engineering discipline
• More than 6 years of experience in data engineering, analytics engineering or data platform delivery
• Strong SQL, Python and PySpark skills, with hands-on experience delivering scalable pipelines and data products
• Ability to move between rapid prototyping and production-grade engineering, and to partner with business stakeholders
• Familiarity with cloud data platforms, orchestration tools and distributed processing patterns

Required skills

• SQL, Python and PySpark (required)
• ETL/ELT pipelines, orchestration workflows, data quality checks and data modelling
• Cloud data platforms, distributed processing, data modelling and analytics/BI consumption patterns
• DevOps and DataOps practices, including CI/CD, monitoring, observability and incident support
• Exposure to AI/ML engineering, feature pipelines, model productionization, LLM-based applications or agentic AI is an added advantage