Business Intelligence Engineer

Who We Are SiriusPoint is a specialty underwriter providing solutions to clients and brokers around the world. Bermuda-headquartered with offices in New York, London, Stockholm and other locations, we are listed on the New York Stock Exchange (SPNT). We have licenses to write Property & Casualty and Accident & Health insurance and reinsurance globally. Our offering and distribution capabilities are strengthened by a portfolio of strategic partnerships with Managing General Agents and program managers. With over $3.0 billion total capital, SiriusPoint’s operating companies have a financial strength rating of A from AM Best, Fitch and S&P, and A3 from Moody’s. Join Our Team As our Business Intelligence Engineer, you will model and transform governed enterprise data into meaningful insights and analytics for SiriusPoint. You will create and maintain trusted data sources, reports, and dashboards for key stakeholders across the organization. You will help evolve SiriusPoint's BI practice from traditional reporting to AI-driven decision intelligence, helping design, build, and govern enterprise analytical datasets, semantic models, and business ontologies that enable scalable self-service analytics and support AI use cases. Leveraging Microsoft Fabric, Power BI, and emerging AI capabilities, you will turn enterprise data into business-ready data products. You will report to the AVP of Business Intelligence. Your responsibilities will include: Data Acquisition and Management: Collaborate with internal stakeholders to identify and understand business needs and translate them into actionable data requests. Extract and transform data from various insurance-specific sources (policy systems, claims databases, and market research) using AWS Redshift and ETL tools. Ensure data quality and integrity through data cleansing, validation, and standardization. Enterprise Analytical Data Products: Design and develop curated analytical datasets and data products that support enterprise reporting, self-service analytics, and AI consumption. Construct scalable semantic gold-layer datasets from enterprise data platforms. Define business-friendly data structures that abstract technical complexity from business users. Help evolve the practice from static report production toward AI-augmented insight, such as automated anomaly detection, forecasting, and plain-language explanation of results. Enforce review and validation standards for AI-assisted development and AI-generated insights so that outputs remain accurate, explainable, and auditable. Strategic Initiatives: Participate in cross-functional projects to leverage data for strategic initiatives, such as product development, pricing optimization, and claims management. Partner with IT to support data infrastructure enhancements and develop new data solutions. Stay abreast of emerging technologies and trends in the insurance and data analytics landscape. Drive adoption of data…