Exposure Data Manager

Role Description

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

• 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

Company Description