Job Description

We are seeking a highly skilled

Data Engineer

with deep expertise in

data engineering concepts, cloud platforms, engineering and operational excellence, and ML Ops

practices. The candidate will play a key role in designing, building, and optimizing scalable data infrastructure, enabling efficient data flows across the organization, and supporting machine learning initiatives.

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Responsibilities

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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

.

Required Skills and Qualifications

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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).

Preferred Qualifications

----------------------------

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.

Education

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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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