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 PySpark 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.