P&C Data Modeler

Key duties and responsibilities
Under the responsibility of the P&C Lead Data Engineer, your mission will be to:
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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;
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Develop and evolve analytical data models that enable scalable reporting, self-service analytics, and AI-driven use cases;
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Contribute to the enterprise P&C data model, ensuring consistent business definitions and alignment across domains and systems;
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Collaborate with Data Engineers, Architects, and P&C stakeholders to ensure models reflect business semantics and integrate into the overall data architecture;
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Ensure data consistency, reconciliation capability, and proper handling of granularity, time dimensions, and historical tracking in financial datasets;
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Support metadata management, business glossaries, and data lineage in collaboration with data stewards to provide transparent and governed P&C data;
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Apply and promote data modeling standards and best practices to improve quality, reusability, and maintainability of P&C data assets.
Required experience & competencies
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5+ years of experience in data modeling or data engineering, with a focus on understanding conceptual, logical, and physical models;
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Experience building analytical data models for reporting and analytics;
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Proficiency in SQL and/or PySpark, with understanding of modern data platform architectures;
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Experience in the insurance industry or in financial services is a strong plus;
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Experience with Databricks and/or Palantir Foundry is a plus.
Behavioral & Management Skills :
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Understanding of data governance, metadata management, and data quality principles;
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Strong analytical thinking, attention to detail, and ability to structure complex topics;
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Ability to collaborate effectively with both technical and business stakeholders;
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Excellent communication skills and ability to operate in an international, matrix environment.
Required Education
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MSc or PhD in computer or data science, software or computer engineering, applied math, physics, statistics, or a related field or equivalent experience.