Azure & AI Data Architect
Azure & AI Data Architect
Location: UK Hybrid / Remote
Type: Full-Time
Must have or be eligible for SC Clearance (no sponsorship can be offered)
Key Responsibilities
- Design and implement enterprise-scale data platforms using Microsoft Azure
- Define data architecture, governance, security, and best practices
- Lead the architecture and delivery of AI, machine learning, and generative AI solutions
- Design modern data lake, lakehouse, and data warehouse architectures
- Develop scalable solutions using Azure Data Factory, Azure Synapse Analytics, Microsoft Fabric, Azure Databricks, and related services
- Collaborate with business and technical stakeholders to create data strategies and roadmaps
- Ensure solutions meet performance, resilience, compliance, and security requirements
- Provide technical leadership and mentoring to engineering and delivery teams
- Evaluate emerging technologies and recommend innovative approaches to maximise business value
.
Required Skills & Experience
Proven experience as a Data Architect, Solution Architect, or Enterprise Data Architect
Strong expertise across the Microsoft Azure data ecosystem
Experience designing and implementing data lakes, data warehouses, and modern analytics platform s.
Hands-on experience wi th:
Azure Data Factory
Azure Synapse Analytics
Microsoft Fabric
Azure Databricks
Azure Data Lake Storage Azure SQL
Power BI
Strong understanding of data governance, security, and compliance frameworks.
Experience with AI and Machine Learning solutions on Azure, including Azure AI Services and Azure Machine Learning.
Knowledge of Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI governance frameworks.
Strong stakeholder management and communication skills.
Experience working within Agile delivery environments .
Desirable
- Microsoft Azure certifications such
- Azure Solutions Architect Expert
- Azure Data Engineer Associate
- Azure AI Engineer Associate
- Experience with MLOps, DevOps, and CI/CD practices.
- Knowledge of Python, SQL, Spark, and data engineering
- frameworks.
- Experience with enterprise architecture methodologies such as TOGAF.