About the Role: As the Exposure Data Analytics Lead, you will: - Lead analytics and investigations that help the business understand exposure patterns, accumulation risk, data completeness, and quality - Define and enforce data standards, quality controls, and best practices for exposure data across business lines - Collaborate with engineers, data platform, and analytics teams to enable downstream use (modeling, reporting, dashboards, catastrophe analytics) Key Responsibilities - Lead the oversight into ingestion, transformation, and normalization of exposure data from internal and external sources - Validate and QA exposure data: identify anomalies, gaps, duplicates, inconsistencies, and drive improvements - Partner with actuarial, underwriting, catastrophe modeling, and product teams to understand their exposure needs - Develop and maintain exposure data documentation, data dictionaries, and process guidelines - Enable and support analytical use cases (e.g. accumulation risk, portfolio stress testing, scenario analysis) - Build, monitor and track data quality KPIs, build dashboards or alerts to surface issues proactively - Support ad hoc analysis to diagnose exposure trends and concentration risk - Provide guidance on integrating exposure data into downstream tools (e.g. modeling engines, pricing systems, BI) Qualifications / Skills Required: - Bachelor’s degree in a quantitative discipline (mathematics, statistics, engineering, computer science, actuarial science) - 3–4+ years of experience with exposure / insurance loss / policy data or related domain - Strong proficiency in SQL; complex query and data transformation skills - Experience in insurance / reinsurance / specialty lines - Familiarity with exposure modeling or catastrophe modeling workflows - Experience with cloud data warehouses like Snowflake - Experience working with ADP data tools or equivalent - Experience in data cleansing, validation, and QA - Analytical mindset with strong problem-solving skills - Excellent communication skills with both technical and non-technical stakeholders - Self-starter with strong ownership and initiative Preferred: - Familiarity with MGA and delegated authority exposure data - Python or R experience for data manipulation and validation - Version control and orchestration tools (Git, dbt, Airflow) - Data governance or metadata management experience