P&C Data Modeler

Key duties and responsibilities Under the responsibility of the P&C Lead Data Engineer, your mission will be to: Design, build, and maintain conceptual, logical, and physical data models for P&C within the Data Foundation (Databricks & Palantir Foundry), supporting reinsurance processes such as underwriting, pricing, claims; Develop and evolve analytical data models that enable scalable reporting, self-service analytics, and AI-driven use cases; Contribute to the enterprise P&C data model, ensuring consistent business definitions and alignment across domains and systems; Collaborate with Data Engineers, Architects, and P&C stakeholders to ensure models reflect business semantics and integrate into the overall data architecture; Ensure data consistency, reconciliation capability, and proper handling of granularity, time dimensions, and historical tracking in financial datasets; Support metadata management, business glossaries, and data lineage in collaboration with data stewards to provide transparent and governed P&C data; Apply and promote data modeling standards and best practices to improve quality, reusability, and maintainability of P&C data assets.

Required experience & competencies 5+ years of experience in data modeling or data engineering, with a focus on understanding conceptual, logical, and physical models; Experience building analytical data models for reporting and analytics; Proficiency in SQL and/or Py. Spark, with understanding of modern data platform architectures; Experience in the insurance industry or in financial services is a strong plus; Experience with Databricks and/or Palantir Foundry is a plus.

Behavioral & Management Skills : Understanding of data governance, metadata management, and data quality principles; Strong analytical thinking, attention to detail, and ability to structure complex topics; Ability to collaborate effectively with both technical and business stakeholders; Excellent communication skills and ability to operate in an international, matrix environment.

Required Education MSc or PhD in computer or data science, software or computer engineering, applied math, physics, statistics, or a related field or equivalent experience.