Senior Data Engineer
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 an advantage
Experience with DevOps and DataOps practices, including CI/CD, monitoring, observability, and incident support
Maersk is committed to a diverse and inclusive workplace, and we embrace different styles of thinking. Maersk is an equal opportunities employer and welcomes applicants without regard to race, colour, gender, sex, age, religion, creed, national origin, ancestry, citizenship, marital status, sexual orientation, physical or mental disability, medical condition, pregnancy or parental leave, veteran status, gender identity, genetic information, or any other characteristic protected by applicable law. We will consider qualified applicants with criminal histories in a manner consistent with all legal requirements.
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