• Design, build and maintain scalable data pipelines using Azure Databricks/AWS across batch and streaming environments.
• Develop and optimise data models using lakehouse/medallion architecture, Delta Lake and performance-tuning techniques.
• Design and deliver analytics solutions, dashboards and reporting using Microsoft Power BI to support manufacturing and quality decisions.
• Translate manufacturing and quality challenges into effective analytical solutions, identifying trends, root causes and improvement opportunities.
• Design and develop AI Agent and LLM-powered solutions, including RAG, agentic workflows, multi-step reasoning and tool/function calling.
• Integrate AI Agents and LLMs with enterprise data pipelines, analytics platforms, APIs and existing business systems.
• Own solutions from requirements and problem definition through development, deployment and ongoing support, proactively removing blockers.
• Work closely with manufacturing, quality and engineering stakeholders while ensuring solutions meet GMP expectations around data integrity, traceability and change control.

Job Requirements

• Bachelor's degree in Computer Science, Engineering, Data Science or a related discipline, or equivalent practical experience.
• Hands-on experience with Azure Databricks or an equivalent Spark-based platform.
• Practical experience developing analytics solutions, dashboards, reporting and data models used for business decision-making.
• Experience developing AI solutions using Agents and/or LLMs, including RAG, prompt engineering, agent frameworks or tool/function calling.
• Strong proficiency in Python and SQL, with familiarity with modern data engineering tools such as Delta Lake, Databricks Workflows and Git-based CI/CD.
• Strong self-starter with proven ability to independently drive projects from concept through to completion.
• Excellent communication skills, with the ability to translate technical concepts into practical solutions for non-technical stakeholders.
• Experience in manufacturing, pharmaceutical or life sciences environments, with knowledge of GMP, data governance, MLOps/LLMOps, vector databases or additional cloud platforms considered an advantage.
• 
  

• Design, build and maintain scalable data pipelines using Azure Databricks/AWS across batch and streaming environments.
• Develop and optimise data models using lakehouse/medallion architecture, Delta Lake and performance-tuning techniques.
• Design and deliver analytics solutions, dashboards and reporting using Microsoft Power BI to support manufacturing and quality decisions.
• Translate manufacturing and quality challenges into effective analytical solutions, identifying trends, root causes and improvement opportunities.
• Design and develop AI Agent and LLM-powered solutions, including RAG, agentic workflows, multi-step reasoning and tool/function calling.
• Integrate AI Agents and LLMs with enterprise data pipelines, analytics platforms, APIs and existing business systems.
• Own solutions from requirements and problem definition through development, deployment and ongoing support, proactively removing blockers.
• Work closely with manufacturing, quality and engineering stakeholders while ensuring solutions meet GMP expectations around data integrity, traceability and change control.

Job Requirements

• Bachelor's degree in Computer Science, Engineering, Data Science or a related discipline, or equivalent practical experience.
• Hands-on experience with Azure Databricks or an equivalent Spark-based platform.
• Practical experience developing analytics solutions, dashboards, reporting and data models used for business decision-making.
• Experience developing AI solutions using Agents and/or LLMs, including RAG, prompt engineering, agent frameworks or tool/function calling.
• Strong proficiency in Python and SQL, with familiarity with modern data engineering tools such as Delta Lake, Databricks Workflows and Git-based CI/CD.
• Strong self-starter with proven ability to independently drive projects from concept through to completion.
• Excellent communication skills, with the ability to translate technical concepts into practical solutions for non-technical stakeholders.
• Experience in manufacturing, pharmaceutical or life sciences environments, with knowledge of GMP, data governance, MLOps/LLMOps, vector databases or additional cloud platforms considered an advantage.
•