Jobs → Data & Analytics → AI Data Analyst (Snowflake SQL)

About the Role The AI Augmented Data Analyst will design and deliver advanced analytics solutions using Snowflake SQL and modern data analysis tools to support property and casualty insurance stakeholders in a hybrid work model. This role focuses on transforming complex datasets into actionable insights that improve risk assessment, pricing accuracy, operational efficiency, and customer outcomes while ensuring robust data quality and compliance. Key Responsibilities • Leverage Snowflake SQL to design, optimize, and maintain scalable data queries and data transformations that reliably support complex analytical use cases for business stakeholders in a hybrid work environment. • Perform comprehensive exploratory data analysis on large structured and semi structured datasets to uncover trends, anomalies, and correlations that influence insurance portfolio performance and operational decision making. • Build, validate, and maintain analytical datasets and data marts that serve as trusted foundations for reporting, dashboards, and advanced analytics across underwriting, claims, and finance teams. • Develop clear, well structured analytic outputs such as reports and visualizations using enterprise data analysis tools to communicate insights and recommended actions to non technical audiences. • Collaborate closely with business teams to translate ambiguous questions into measurable analytical problems, defining metrics, data requirements, and success criteria that align with organizational objectives. • Apply AI augmented analytics techniques to accelerate data profiling, pattern detection, and insight generation while carefully validating outputs to ensure accuracy, fairness, and business relevance. • Implement robust data quality checks, reconciliation routines, and exception monitoring that safeguard the integrity of critical decision support datasets and regulatory reporting processes. • Document data lineage, business rules, and analytical methodologies in a clear and accessible manner so that colleagues can reliably reuse, audit, and extend existing analytical solutions. • Contribute to continuous improvement of data analysis workflows by proposing enhancements to query designs, data models, and tool usage that reduce cycle times and improve insight depth. • Partner with technology and data engineering teams to refine data pipelines, source system integrations, and performance tuning efforts so that analytics workloads remain stable and responsive during day shifts. • Support stakeholders with ad hoc analyses that respond to time sensitive questions about portfolio performance, operational bottlenecks, and customer behavior while maintaining methodological rigor. • Champion ethical and responsible use of data and AI augmented techniques by highlighting potential biases, recommending mitigation strategies, and aligning analytics initiatives with positive societal outcomes. • Engage in knowledge sharing activities such as walkthroughs and peer reviews to strengthen analytic standards, foster innovation, and ensure consistent data driven practices across the organization.