Business Data Insights Senior Specialist

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Skills you'll use

What you'll do

• Role: Data & AI Engineer
• 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.

Who can apply

• 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.

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