Data Engineer

• Design, develop, and maintain scalable data pipelines and ETL/ELT processes to process structured and unstructured data.
• Implement robust data models, data lakes, and data warehouses that enable analytics and machine learning use cases.
• Collaborate with software engineers, data scientists, and business stakeholders to deliver high-quality, production-ready data solutions.
• Ensure data quality, governance, and lineage tracking across all data assets.
• Drive operational excellence through automation, monitoring, and alerting for data systems.
• Apply engineering best practices (CI/CD, testing frameworks, code reviews, performance optimization) to ensure reliability and maintainability of data workflows.
• Partner with ML engineering and research teams to establish ML Ops pipelines, ensuring reproducibility, model deployment, monitoring, and scalability in production environments.
• Optimize cloud-based data platforms (e.g., AWS, Azure, GCP) for cost, performance, and security.
• Contribute to internal standards, documentation, and guidelines for data engineering excellence.

• Strong foundation in data engineering concepts: data modeling, pipelines, batch/streaming (Kafka, Spark, Flink, or similar).
• Proficiency in cloud services (AWS, Azure, or GCP) with solid experience in cloud-native data tools (e.g., BigQuery, Redshift, Synapse, Databricks, Snowflake).
• Hands-on expertise with modern data orchestration frameworks (Airflow, Prefect, Dagster).
• Strong coding skills in Python, SQL, and one additional language (Scala/Java preferred).
• Experience with engineering excellence practices: version control, CI/CD, unit/integration testing, observability, and performance optimization.
• Background in operational excellence methodologies (SRE principles, system reliability, monitoring, alerting).
• Familiarity with ML Ops frameworks (MLflow, Kubeflow, Vertex AI, or Azure ML) and ability to work closely with ML engineers.
• Understanding of containerization and orchestration (Docker, Kubernetes).
• Knowledge of data governance and compliance best practices (security, access management, GDPR/PII handling).

• Experience in designing large-scale data platforms serving both analytics and AI/ML needs.
• Exposure to real-time streaming architectures.
• Familiarity with DevOps principles in the context of data and machine learning workflows.
• Strong problem-solving skills, with an emphasis on scalability and reliability.
• Excellent communication skills and ability to work in cross-functional, global teams.

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or related field.

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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