Business Data Insights Senior Specialist

We are looking for a skilled and versatile Data & AI Engineer who can own the full data-to-AI lifecycle designing and building robust data pipelines using Azure Data Lake Azure Data Factory and Azure Databricks and then using that data to build fine-tune and deploy machine learning and generative AI solutions with Azure Machine Learning and Azure OpenAI.
The role requires strong hands-on experience across both disciplines: developing ETL/ELT pipelines transforming structured and semi-structured data and ensuring reliable data availability as well as building RAG pipelines integrating LLM-based solutions and operationalizing models with MLOps best practices.
The candidate should be comfortable working with business stakeholders data scientists BI teams product teams and technical teams to understand requirements design scalable data and AI solutions and deliver measurable business value end to end from raw data to production-ready AI features.

Job Description

Role: Data & AI Engineer

Work Experience

5 years of combined experience in data engineering and AI/ML engineering including building scalable data pipelines and cloud data platforms as well as developing and deploying machine learning or generative AI solutions in an Agile or DevOps environment.

We are looking for a skilled and versatile Data & AI Engineer who can own the full data-to-AI lifecycle designing and building robust data pipelines using Azure Data Lake Azure Data Factory and Azure Databricks and then using that data to build fine-tune and deploy machine learning and generative AI solutions with Azure Machine Learning and Azure OpenAI.

The role requires strong hands-on experience across both disciplines: developing ETL/ELT pipelines transforming structured and semi-structured data and ensuring reliable data availability as well as building RAG pipelines integrating LLM-based solutions and operationalizing models with MLOps best practices.

The candidate should be comfortable working with business stakeholders data scientists BI teams product teams and technical teams to understand requirements design scalable data and AI solutions and deliver measurable business value end to end from raw data to production-ready AI features.

• 4 years of experience spanning data engineering and AI/ML engineering with strong exposure to data integration ETL/ELT development and cloud-based data and AI platforms.
• Hands-on experience with Azure Data Factory for building scheduling monitoring and managing data pipelines.
• Working knowledge of Azure Data Lake Storage for storing organizing and managing large volumes of data.
• Experience with Azure Databricks including notebooks Spark SQL PySpark data transformation and performance optimization.
• Strong SQL and Python skills for querying transformation data validation troubleshooting and performance tuning.
• Hands-on experience with Azure Machine Learning or Azure AI Studio for training deploying and managing ML models.
• Practical experience with Azure OpenAI Service or similar LLM platforms including prompt engineering fine-tuning and model integration.
• Experience building RAG pipelines using vector databases (e.g. Azure AI Search FAISS Pinecone) for grounding LLM responses in enterprise data.
• Experience with ML/AI frameworks such as PyTorch TensorFlow scikit-learn LangChain or Semantic Kernel.
• Good understanding of data lake architecture (bronze silver gold / medallion architecture) and MLOps practices (model versioning CI/CD for ML monitoring retraining).
• Experience with data modelling data quality checks data profiling reconciliation feature engineering and model evaluation.
• Exposure to DevOps practices such as Git CI/CD containerization (Docker Kubernetes) version control and deployment processes.
• Working knowledge of Power BI or similar reporting tools and knowledge of legacy ETL tools (Informatica SSIS Teradata Oracle) will be an added advantage.

• Design develop test deploy and maintain scalable data pipelines using Azure Data Factory Azure Data Lake Azure Databricks SQL and related technologies.
• Build automated ETL/ELT pipelines to ingest transform validate and publish data for analytics reporting and AI model consumption.
• Design build and fine-tune machine learning and generative AI models to solve business problems using the curated data pipelines as the foundation.
• Develop RAG pipelines and integrate LLM-based solutions with enterprise data sources and applications.
• Work with structured semi-structured and unstructured datasets preparing them for both analytics and model training/evaluation.
• Develop data and model transformation logic using SQL PySpark Spark SQL Databricks notebooks and Python ML frameworks.
• Create and maintain reliable data flows and AI services across raw curated and consumption-ready layers including production model inference.
• Perform data exploration validation reconciliation model evaluation and performance testing to ensure accuracy and reliability end to end.
• Collaborate with business stakeholders data scientists BI developers product owners and architects to convert requirements into technical data and AI solutions.
• Support data migration system integration and AI feature rollout from legacy platforms to cloud-based data and AI platforms.
• Monitor pipeline and model performance in production troubleshoot failures optimize processing/inference cost and latency and ensure timely availability.
• Implement data quality rules responsible AI practices exception handling logging audit checks and operational controls.
• Contribute to documentation of data flows model architecture source-to-target mappings technical designs and operational support procedures.
• Participate in E2E product lifecycle activities including design development testing deployment run support retraining refactoring and decommissioning of outdated solutions.
• Continuously improve existing data and AI engineering processes through automation standardization and reusable components.

• Strong understanding of both data engineering and AI/ML concepts data pipelines data integration data modelling model lifecycle and cloud-based platforms.
• Ability to understand business requirements and translate them into scalable technical designs spanning data and AI.
• Experience in working with data structures storage systems model architectures data quality frameworks and system integrations.
• Good understanding of enterprise data flows upstream and downstream dependencies and AI/reporting consumption patterns.
• Ability to quickly analyze existing data and AI solutions and recommend improvements automation opportunities or alternative approaches.
• Strong problem-solving skills with the ability to troubleshoot issues across pipelines databases models and application layers.
• Experience working in Agile teams with E2E ownership of deliverables from data ingestion through to deployed AI features.
• Ability to work with both technical and non-technical stakeholders.
• Good documentation skills including technical design documents model cards process flows data mapping and support guides.

• Strong team player with the ability to work independently when required.
• High ownership mindset with a focus on delivering reliable and scalable data and AI solutions.
• Open to learning new technologies and applying them to improve existing processes.
• Strong analytical thinking and attention to detail.
• Ability to understand end-to-end business processes and data/AI dependencies.
• Good communication skills with the ability to explain technical data and AI concepts in a simple and clear manner.
• Proactive approach toward automation optimization and continuous improvement.

• Graduate or postgraduate degree in Computer Science Information Technology Data Engineering Artificial Intelligence Data Science or a related field.
• Relevant certifications in Azure Data Engineering Azure Databricks Azure Data Factory Azure Machine Learning or generative AI technologies will be an added advantage.

Cloud & Data Platform: Azure Data Lake Azure Data Factory Azure Databricks

Cloud & AI Platform: Azure Machine Learning Azure AI Studio Azure OpenAI Service

Programming & Querying: SQL Python PySpark Spark SQL

AI/ML Frameworks: PyTorch TensorFlow scikit-learn LangChain Semantic Kernel

Data & AI Engineering: ETL ELT data pipelines data modelling data quality RAG pipelines prompt engineering MLOps

Vector Search & Data: Azure AI Search FAISS Pinecone vector databases

BI & Reporting: Power BI SSRS or equivalent visualization tools

DevOps & Delivery: Git Azure DevOps CI/CD Docker Kubernetes Agile delivery practices

Legacy or Enterprise Systems: Informatica SSIS Oracle Teradata SQL Server

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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