Senior Data Engineer

Job Summary

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.

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

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.