Our Client is one of the United States’ largest insurers, providing a wide range of insurance and financial services products with gross written premiums well over US$25 Billion (P&C;). They proudly serve more than 10 million U.S. households with more than 19 million individual policies across all 50 states through the efforts of over 48,000 exclusive and independent agents and nearly 18,500 employees. Finally, our Client is part of one the largest Insurance Groups in the world.
We are seeking a highly skilled and motivated Semantic Data Modeler to join our data and analytics team. In this role, you will design and develop a robust semantic layer and underlying data models that enable intuitive, self-service access to data across the organization. Your work will bridge the gap between complex technical data and clear business language, empowering teams to make data-driven decisions with confidence.
Key Responsibilities:
- Collaborate with data architects to align semantic layer design with overall data strategy.
- Design and develop semantic data models that reflect business concepts and relationships.
- Translate technical data into simple, business-friendly language and glossaries.
- Apply data virtualization principles to integrate and model data from multiple sources in a cloud environment.
- Define and implement data quality checks, transformations, and cleansing rules.
- Document semantic models, including entity definitions, relationships, and data lineage.
- Work with business and technical stakeholders to understand needs and ensure semantic consistency.
- Optimize semantic models for performance and usability in self-service analytics.
- Ensure data access and security measures align with organizational policies.
- Stay current with trends in data modeling, semantic technologies, and cloud-based analytics.
Required Skills & Qualifications:
- Proficiency in SQL and relational databases.
- Strong understanding of data quality, data governance, security, and
📌 FBS Semantic Data Modeler (México)
🏢 Capgemini
📍 México
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