Required Skills as per highlight (Duck creek is good to have not mandate)
· Insurance Expertise: Minimum 5+ years of experience working specifically with Personal Lines data (Automobile, Homeowners, Personal Property).
· Duck Creek Clarity: Deep structural understanding of Duck Creek Clarity schemas, transactional databases, and operational data stores.
· Cloud Data Warehousing: Expert-level design experience with Snowflake, including data sharing, clustering, and cost optimization.
· Relational Databases: Strong mastery of PostgreSQL implementation, indexing, and complex query optimization.
· Programming: Advanced proficiency in Python for data manipulation, scripting, data loading to Duck Creek and ETL frameworks.
· Data Migration Experience: Proven track record of handling large-scale core system migrations with zero data loss.
· Data Quality Tools: Experience with DQ frameworks using custom Python/PostgreSQL-based validations.
Job Overview
We are seeking a Senior Data Architect to design and modernize data platforms across personal insurance lines, specifically Automobile, Personal, and Homeowners insurance. You will lead the modern migration of legacy core systems Mainframe and DB2 into a high-performance Duck Creek cloud ecosystem. Your primary focus will be architecting data solutions using Snowflake, PostgreSQL, and Python, with a specific emphasis on Duck Creek Clarity policy, billing, and claims data models. You will establish robust Data Quality (DQ) frameworks to ensure accurate underwriting analytics, risk profiling, and regulatory reporting.
Domain-Specific Responsibilities
· Multi-Line Integration: Architect data models that unify distinct data streams across Auto (vehicle telematics, driving history) and Homeowners (property risk, geographic data) lines of business.
· Duck Creek Integration: Design and manage ingestion pipelines from Duck Creek Clarity to extract, transform, and load comprehensive policy, billing, and claims workflows.
· Data Migration: Lead complex migration strategies transferring high-volume personal lines historical data from legacy mainframes or relational databases to modern cloud platforms.
· Data Quality Frameworks: Establish automated DQ rules and profiling to ensure pristine financial reporting, accurate loss-ratio calculations, and claims tracking.
· Pipeline Development: Build efficient, reusable data ingestion and transformation pipelines using Python and SQL.
· Predictive Analytics Support: Partner with Actuarial and Underwriting teams to structure data for pricing models, geographic hazard mapping, and customer lifetime value analytics
