Must have skills:

Logical Modelling & EDM: Proven expertise in building Logical Data Models and ensuring 100% mapping and alignment with an Enterprise Data Model (EDM).
Data Governance & Stewardship: Strong experience in governing data products, defining data modelling standards, championing Data Lineage, Metadata Management, and managing model release/change controls.
Modelling Tools: Hands-on exposure and high proficiency with enterprise Data Modelling Tools (e.g., ERwin, Power Designer, ER/Studio, Enterprise Architect).
Data Architecture: Solid understanding of data warehousing principles, OLTP, OLAP, Data Lakes, and dimensional data models for analytics consumption.
Data Analysis: Excellent skills in data profiling, creating Source-to-Target mappings, writing functional specifications, and building comprehensive Data Dictionaries.
Cloud Platforms: Experience working with modern cloud data platforms, specifically Google Cloud Platform (GCP)
Advanced Modelling Techniques: Experience with Data Vault modelling approaches for Operational Data Stores.

Good to Have Skills

Domain Expertise: Deep understanding of the Insurance domain and Industry Standard Data Models (e.g., ACORD), covering areas like Customer 360, Policy Lifecycle, Claims, and Underwriting.
Experience working with Microsoft Azure.
AI/ML Context: An understanding of how logical data products feed into advanced analytics and AI/ML use cases (e.g., Feature Stores, predictive modelling).
Data Tools: Familiarity with ETL/ELT processes, data ingestion frameworks, and data profiling tools (e.g., Informatica Analyst).
Agile & Automation: Good understanding of Agile methodologies and a track record of driving automation within data modelling activities.
Stakeholder Management: Excellent communication and facilitation skills. Ability to assertively enforce data standards with external Use Case Implementation teams, Business Stakeholders, and Data Owners.

Data Modeler