• Lead, coach, and develop a high-performing analytics team • Drive end-to-end analytical strategy and delivery • Own standardized regional and global reporting frameworks • Partner with global and regional stakeholders for analytics solutions • Manage the regional Power BI environment • Oversee integration of enterprise source systems into reporting architecture • Collaborate cross-functionally to consolidate data and deliver insights • Ensure consistency of reporting by aligning regions to global frameworks

• Provide analytical support to the Quantitative Analytics function within Claims by developing, maintaining, and updating quantitative models, tools, and reports related to claims activity. • This role focuses on lower-complexity modeling, preparatory analysis, and data organization, working under the direction of the Quantitative Analytics Manager and in partnership with Claims and Actuarial teams. • Develop and update quantitative models in Microsoft Excel to support claims analysis, projections, and scenario evaluation. • Perform lower-complexity modeling and assist with preparatory work for more complex analyses under the guidance of the Quantitative Analytics Manager. • Support reserve-related analyses in coordination with Actuarial and Claims teams. • Assist in building and maintaining settlement trackers, allocation calculators, and reporting tools. • Ensure tools are structured to be scalable and efficient as data volume increases. • Support preparation of recurring reports related to claim payments, reserving activity, and remaining limits. • Convert externally provided data into standardized internal formats. • Assist with system enhancements, data imports/exports, and maintenance of analytical tools in Excel or Access.

Role Description The Analytics Data Modeler plays a vital role in transforming raw data into meaningful business insights, which is essential for unlocking the value of data within organizations. This professional is responsible for designing, developing, and maintaining robust data models that empower organizations to make informed decisions based on accurate and accessible information. Working at the intersection of business needs, data architecture, and advanced analytics, the Analytics Data Modeler ensures that data flows seamlessly and can be leveraged to derive actionable intelligence to empower teams to uncover insights, drive strategy, and achieve business success in the data-driven age. What you will be doing - Data Modeling and Design: Develop conceptual, logical, and physical data models for business intelligence, analytics, and reporting solutions. Transform requirements into scalable, flexible, and efficient data structures that can support advanced analytics. - Requirement Analysis: Collaborate with business analysts, stakeholders, and subject matter experts to gather and interpret requirements for new data initiatives. Translate business questions into data models that can answer these questions. - Data Integration: Work closely with data engineers to integrate data from multiple sources, ensuring consistency, accuracy, and reliability. Map data flows and document relationships between datasets. - Database Architecture: Design and optimize database schemas using the medallion architecture which includes relational, star schema and denormalized data sets for BI and ML data consumers. - Metadata Management: Team with the data governance team so detailed documentation on data definitions, data lineage, and data quality statistics are available to data consumers. - Data Quality Assurance: Establish master data management and data modeling practices that preserve historical context, explain data changes resulting from remediation or repairs, and enable consumers to understand variances from source systems. - Collaboration and Communication: Serve as a bridge between technical teams and business units, clearly communicating the value and limitations of various data sources and structures. - Continuous Improvement: Stay abreast of emerging trends in data modeling, analytics platforms, and big data technologies. Recommend enhancements to existing data models and approaches. - Performance Optimization: Monitor and optimize data models for query performance and scalability. Troubleshoot and resolve performance bottlenecks in collaboration with database administrators. - Governance and Compliance: Ensure that data models and processes adhere to regulatory standards and organizational policies regarding privacy, access, and security. Qualifications - Bachelor’s degree in Computer Science, Information Systems, Data Science, Mathematics, or related field. Master’s degree preferred. - Deep familiarity with modern data modeling techniques, database management, and analytics platforms. - Minimum 3 years of experience in data modeling in a Lakehouse analytics environment. - Experience in insurance industry which can be P&C, Life, or healthcare. - Proficiency in a data modeling tool such as ER Studio. - Solid understanding of DataBricks Delta tables and Postgres database platforms. - Experience with big data technologies and platforms (e.g., DataBricks, Spark, AWS, Azure). - Expert level in DDL and SQL development. - Experience with data governance, quality frameworks, and metadata management, and how this connects to data modeling practices and needs. - Strong analytical, problem-solving, and critical thinking skills needed for data modeling. - Excellent communication and interpersonal abilities. - Ability to understand business processes, goals, and pain points to design data models that solve real-world problems. - Strong team player who can interact with developers, analysts, and non-technical stakeholders. - Ability to thrive in a fast-paced, dynamic environment and quickly pivot between projects. Requirements - Understanding of PII and PHI data and how they are governed and secured. - Experience working on an agile team using agile tools, and associated practices. - Knowledge of business intelligence tools (e.g., Power BI, Spotfire). - Data taxonomies in insurance down to two or three levels. - Knowledge of typical data quality issues with insurance data. - Knowledge of cloud data management, data lakes, and ETL (Extract, Transform, Load) processes. - Ability to work independently and collaboratively in a cross-functional team environment. - Strong attention to detail and commitment to data accuracy. Compensation Range Within our company the base salary pay range for the job level P4 starts at a minimum rate of $120,000 up to the maximum of $195,000. The final determination on base pay for this position will be based on multiple factors to ensure pay equity within the organization. In addition to your base salary, you will also be eligible to participate in an Incentive Plan. The Incentive Plan is based on performance and the target earning opportunity is 15%. Benefits - Competitive Compensation & Total Rewards Incentives - Comprehensive Healthcare Coverage - Multiple 401(k) Savings Plan Options - Auto Enrollment in Employer-Directed Retirement Account Feature (100% employer-funded!) - Generous Paid Time Off – Including 12 Paid Holidays, Volunteer Time Off and Paid Family Leave - Disability, Life, and Long Term Care Insurance - Tuition Reimbursement, Student Loan Repayment and Training & Certification Support - Wellness support including gym membership reimbursement and Employee Assistance Program resources (work/life support, financial & legal management) - Caregiver and Mental Health Support Services

• Define and evolve enterprise data, analytics, and AI architecture standards, target and reference architectures • Design and guide end-to-end architectures covering data ingestion, analytics, ML and AI platforms • Be a technical leader viewed as a thought leader for innovation in data, machine learning and AI areas • Identify and research data, analytics and AI relevant technologies, perform proofs of concept / prototypes • Create and grow architectural capability across the organization • Develop and maintain strong working relationships with internal stakeholders • Stay abreast of industry technical and business trends through participation in professional associations, practice communities, and individual learning • Provide architectural expertise and domain knowledge to advise and guide senior leaders