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An exciting opportunity has arisen for a proven Data Architect with deep expertise in Snowflake and extensive knowledge of the insurance industry to lead the design and evolution of our enterprise data infrastructure. This role requires developing scalable, secure, and high-performance data solutions, including data warehouses and lakes, with a focus on Snowflake as the cloud data platform on AWS. You will have a proven track record of migrating significant data workloads-such as claims management and fraud detection-to Snowflake, optimising data pipelines and establishing best practices for data modelling, governance, and security. A strong background as a Subject Matter Expert (SME) in insurance is essential, particularly in domains such as risk management, claims processing, underwriting, fraud detection, and customer analytics. Your industry-specific insights will enable you to design tailored data architectures that support regulatory compliance, operational efficiency, and strategic analytics. Familiarity with insurance data standards, regulatory frameworks, and industry-specific data challenges will be crucial in delivering high-value, compliant solutions.
Job Responsibility:
lead the design and evolution of enterprise data infrastructure
develop scalable, secure, and high-performance data solutions, including data warehouses and lakes, with a focus on Snowflake as the cloud data platform on AWS
optimise data pipelines
establish best practices for data modelling, governance, and security
design tailored data architectures that support regulatory compliance, operational efficiency, and strategic analytics
Requirements:
proven Data Architect with deep expertise in Snowflake
extensive knowledge of the insurance industry
proven track record of migrating significant data workloads (e.g., claims management, fraud detection) to Snowflake
strong background as a Subject Matter Expert (SME) in insurance, particularly in risk management, claims processing, underwriting, fraud detection, and customer analytics
familiarity with insurance data standards, regulatory frameworks, and industry-specific data challenges