Job Description
Job Description
We are looking for a Senior Data Modeler to join a General Insurance Firm. The role is to design and manage enterprise data models for a Databricks-based Lakehouse platform. The role will focus on Data Vault 2.0, dimensional modelling, data governance and building scalable models for reporting, analytics, AI/ML and regulatory requirements. Company: General Insurance Firm Position: Data Modeler Reporting To: Chief Data Architect Experience: 8+ years Location: Navi Mumbai Key Responsibilities: Design conceptual, logical and physical data models across business domains. Build Data Vault 2.0 structures including Hubs, Links, Satellites, PIT and Bridge tables. Develop dimensional models and semantic models for reporting and analytics. Design data models across Policy, Claims, Customer, Finance, Sales, Premium, Commission and Regulatory domains. Define modelling patterns across Bronze, Silver and Gold layers in Databricks Lakehouse. Work with Data Architects and Engineers to ensure scalable and high-performance models. Drive data governance, metadata, lineage, data dictionaries and modelling standards. Translate business requirements and reporting needs into structured data models. Conduct design reviews and mentor junior data modelling teams. Key Skills: Data Vault 2.0 & Enterprise Data Modelling. Databricks Lakehouse, Delta Lake & Unity Catalog. Dimensional Modelling & Data Warehousing. Advanced SQL & Data Analysis. Data Governance, Metadata & Lineage. ETL/ELT and Spark/PySpark fundamentals. Data Modelling tools such as ERwin, SQLDBM, ER Studio or equivalent. Candidate Profile: 8+ years of experience in Data Modelling, Data Architecture, Data Engineering or Enterprise Data Management. Strong hands-on experience in Data Vault 2.0 and dimensional modelling. Experience with cloud-based data platforms, preferably Databricks Lakehouse. Strong stakeholder management and communication skills. Insurance domain experience across Policy, Claims, Customer, Finance or Regulatory Reporting would be preferred.