Senior Data Engineer

Job description

Who are we: At Maersk, we are redefining global logistics through data, platform engineering, and AI-driven innovation. As part of this journey, we are building scalable platforms and intelligent systems that enable faster decision-making, operational efficiency, and seamless integration across the enterprise. The Position: As a Senior Data Engineer, Data & AI, you will design, build, and operate scalable data products, pipelines, and analytical foundations that power critical business capabilities and AI-enabled decision-making. You will work across data engineering, analytics enablement, visualization, and AI/ML engineering, contributing to reliable data pipelines, governed datasets, orchestration frameworks, and data products that enable self-service analytics and intelligent automation across the enterprise. The role requires a hands-on problem solver who can partner with business stakeholders and product teams to understand requirements, build quick prototypes where useful, and evolve validated solutions into production-ready data & AI products. Key Responsibilities: Data Engineering, Pipelines & Orchestration • Design, build, and optimize scalable batch and streaming data pipelines using modern data engineering patterns • Develop robust orchestration workflows for dependable data ingestion, transformation, quality checks, and downstream consumption • Apply strong SQL, Python, and PySpark skills to transform complex data into reliable, reusable, and performant data products SQL, Data Modelling & Analytics Enablement • Create well-modelled, trusted datasets that support reporting, visualization, advanced analytics, and AI/ML use cases • Enable self-service data access and governed consumption by building clear data contracts, documentation, and quality controls • Contribute to integrated data foundations that provide consistent, reusable data across business domains and platforms Visualization, BI & Data Product Delivery • Partner with analytics and product teams to deliver high-quality datasets, dashboards, and visualization-ready semantic layers • Translate business requirements into scalable data models and consumption patterns for operational and executive insights • Support adoption of data products by ensuring performance, usability, reliability, and clear lineage from source to insight AI/ML Engineering Enablement • Build data pipelines and feature-ready datasets that support machine learning, AI, and GenAI use cases • Collaborate with data scientists and AI engineers to productionize models, automate data refreshes, and improve repeatability • Apply engineering practices for monitoring, testing, versioning, and operationalizing data and ML workflows Cross-Functional Delivery & Architecture • Work with Product, Analytics, Platform, Data Science, AI teams, and business stakeholders to clarify requirements and deliver pragmatic end-to-end data solutions • Translate business requirements, user feedback, and problem statements into data models, working prototypes, technical designs, and implementation plans • Contribute to data architecture discussions and ensure alignment with enterprise standards, security, and governance expectations • Support integrations across cloud, and enterprise data ecosystems Operational Excellence • Ensure data solutions are reliable, scalable, performant, secure, and production-ready • Monitor, troubleshoot, and continuously improve pipeline performance, data quality, and platform stability • Drive automation, observability, and supportability across data, analytics, and AI/ML solutions Our Ideal Candidate: • Strong data engineering experience with hands-on delivery of scalable data pipelines, data products, and analytics foundations • Advanced SQL skills with the ability to design performant queries, data models, and transformation logic • Hands-on knowledge of Python and PySpark for large-scale data processing and automation • Curious, hands-on problem solver who can engage with business stakeholders to understand the real requirement and deliver practical outcomes • Comfortable moving between rapid prototyping and production-grade data engineering based on business need • Experience enabling visualization, BI, AI/ML, or advanced analytics through trusted and well-governed data foundations • Familiarity with cloud data platforms, orchestration tools, and distributed data processing patterns • Strong ownership mindset and ability to work effectively across teams Required Skills/Experience: • MS or BS in a Computer Science or a science/engineering discipline. • More than 6 years of experience in data engineering, analytics engineering, or data platform delivery • Strong proficiency in SQL, Python, and PySpark is required • Experience designing and operating ETL/ELT pipelines, orchestration workflows, data quality checks, and production data products • Experience with cloud data platforms, distributed processing, data modelling, and analytics/BI consumption patterns • Exposure to AI/ML engineering practices, feature pipelines, model productionization, LLM-based applications, or agentic AI patterns will be considered