Must have

• SQL

• English (B2)

Nice to have

• Azure Data Factory

• Informatica

• Telend

• Tableau

• Power BI

Requirements description

• Bachelor’s degree in Computer Science, Information Systems, or a related field. Master’s degree preferred.
• Proven experience in data modeling and data warehousing, with at least 2 years of experience in a similar role, preferably within the insurance industry.
• Strong understanding of data modeling techniques, including normalization, denormalization, star schema, and snowflake schema.
• Experience with data modeling tools such as ERwin, Sparx Enterprise Architect, or similar.
• Proficiency in SQL and database management systems (e.g., SQL Server, Oracle).
• Familiarity with ETL processes and tools (e.g., Azure Data Factory, Informatica, Talend).
• Knowledge of data governance and data quality principles.
• Excellent analytical and problem-solving skills, with the ability to perform system analysis to identify and extract relevant data.
• Experience in developing and executing test plans to validate data models.
• Strong communication skills, with the ability to explain complex data concepts to non-technical stakeholders.

Offer description

The Data Modeler will be responsible for designing, implementing, and maintaining data models that support data warehousing solutions within the insurance domain. The team works on Azure, but experience with other cloud platforms is also acceptable.

Your responsibilities

1. Design and develop conceptual, logical, and physical data models to support data warehousing initiatives specific to insurance operations.
2. Collaborate with business analysts, architects, and IT teams to understand insurance business requirements and translate them into data models.
3. Perform system analysis to identify, analyze, and extract the right data from source systems, ensuring the data models are populated with accurate and relevant data.
4. Optimize data models for performance, scalability, and maintainability, focusing on insurance data such as policy, claims, underwriting, and customer data.
5. Implement data modeling standards and best practices tailored to the insurance industry.
6. Conduct data analysis and profiling to understand data quality and integrity, particularly in the context of insurance data.

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