Senior Data Quality Analyst

Ledgebrook is an AI-native specialty insurance platform that uses technology and underwriting expertise to provide custom and complex coverage. The Senior Data Quality Analyst will lead analytical efforts to ensure data integrity and accuracy, investigate discrepancies, strengthen data controls, monitor data health, and collaborate with engineering and business teams to improve data quality.

Responsibilities:

• Investigate and resolve data issues: Run deep-dive investigations to identify, trace, and resolve the root cause of data discrepancies for stakeholders across product, finance, actuarial, and analytics
• Strengthen controls: Find gaps and implement data validations, reconciliations, and controls that improve data quality over time
• Monitor data health: Build and maintain automated checks to track the integrity of policy, premium, and claims data
• Bridge business and engineering: Translate business requirements into clear specifications for Data Engineering, and help define and document shared data definitions
• Scale our data foundation: Design data workflows and system integrations that support Ledgebrook's growth from MGA to full-stack carrier

Requirements:

• Experience analyzing Property & Casualty P&C insurance data (e.g., policy, premium, claims, or submission metrics) in support of actuarial, analytics, or finance functions
• Advanced SQL, Excel, and Power BI for investigation, data modeling, and reporting
• Excellent communication and presentation skills to collaborate with stakeholders across the organization
• Close attention to detail and the curiosity to trace complex data inconsistencies back to their source
• Comfort integrating AI tools in day-to-day workflows to drive efficiencies
• A self-starter who takes accountability, thrives in a fast-paced environment, and handles ambiguity well
• authorized to work in the U.S or Canada without the need for current or future employer-sponsored work authorization
• Familiarity with integrating data with finance/ERP systems (e.g. Workday)
• Experience at an MGA or a carrier that partners with MGAs
• Exposure to Snowflake, Python, Power Query, and/or AI tools that streamline day-to-day workflows