Senior Data Modeler – Databricks Lakehouse
Enterprise Data Platform | Data Vault 2.0 | Delta Lake | Unity Catalog
Role Level: Senior / Lead Individual Contributor
Location: Pune/Trivandrum
Platform Focus: Databricks Lakehouse
Domain Preference: Insurance / Financial Services
Reporting To: Data Architecture Lead / Service Delivery Head
Role Summary We are looking for a Senior Data Modeler to lead enterprise-scale data modelling for a Databricks-based Lakehouse platform. The role will own conceptual, logical and physical data models, with strong focus on Data Vault 2.0, dimensional modelling, Delta Lake, Unity Catalog, metadata management and governed data consumption. This is not a Synapse development role. The focus is on designing scalable, business-aligned and audit-ready data models on Databricks to support regulatory reporting, management reporting, analytics, AI/ML and self-service BI use cases.
Key Responsibilities
- Enterprise Data Modelling • Design conceptual, logical and physical data models across business domains and enterprise data platform layers. • Build scalable Data Vault 2.0 structures including Hubs, Links, Satellites, PIT tables and Bridge tables. • Design dimensional models, star schemas, snowflake schemas and semantic consumption models for reporting and analytics. • Create canonical business models for Customer, Policy, Claims, Finance, Sales, Premium, Commission and Regulatory domains. • Drive source-to-target mapping workshops and translate business definitions into governed data structures.
- Databricks Lakehouse Modelling • Define modelling patterns for Bronze, Silver and Gold layers in the Databricks Lakehouse architecture. • Design data structures optimized for Delta Lake storage, Databricks SQL consumption and scalable analytical workloads. • Collaborate with data architects and data engineers to ensure models are implementable, performant and reusable. • Support model optimization through partitioning, clustering, normalization or denormalization decisions based on use case needs. • Drive standards for layer-wise transformation logic, model ownership and consumption readiness.
- Governance, Metadata and Lineage • Define and enforce enterprise data modelling standards, naming conventions and data definition guidelines. • Support Unity Catalog-based governance including catalog, schema and table organization principles. • Maintain data dictionaries, business glossaries, metadata repositories and model documentation. • Partner with governance and architecture teams to establish lineage, ownership, quality rules and auditability. • Ensure models support regulatory, privacy, security and compliance expectations.
- Business and Stakeholder Engagement • Work closely with business SMEs, product owners, reporting teams, data engineers and architects. • Translate business requirements, reporting needs and regulatory definitions into structured data models. • Conduct data model walkthroughs, design reviews and sign-off discussions with stakeholders. • Identify gaps in source systems, business logic, reference data and mapping documentation. • Support impact assessment for changes in source systems, reports and downstream consumption layers.
- Leadership and Quality Assurance • Mentor junior data modelers and data engineers on modelling standards and reusable design patterns. • Review modelling deliverables for quality, consistency, scalability and governance compliance. • Create reusable templates for source-to-target mapping, data dictionaries and model review checklists. • Represent data modelling in architecture forums, design governance discussions and project ceremonies. • Enable audit-ready documentation and traceability from business requirement to data model to implementation.
Mandatory Technical Skills
Preferred Skills • Azure Data Lake Storage working knowledge, with clear understanding of storage structure and access patterns. • Power BI semantic modelling awareness for downstream consumption design. • Master Data Management and Reference Data Management exposure. • Data quality framework design and validation rule definition. • Insurance data domain exposure across Policy, Claims, Customer, Finance, Sales or Regulatory Reporting. • Data sharing and governed consumption concepts, including Delta Sharing or equivalent patterns.
Required Qualifications • Bachelor’s or Master’s degree in Computer Science, Information Technology, Data Engineering, Data Science, Analytics or a related discipline. • 10+ years of experience in Data Modelling, Data Architecture, Data Engineering or Enterprise Data Management. • Proven hands-on experience in Data Vault 2.0 and dimensional modelling for enterprise analytical platforms. • Experience working on cloud-based data platforms, preferably Databricks Lakehouse. • Strong communication skills with ability to engage business, technical and governance stakeholders.
Preferred Certifications • Certified Data Vault 2.0 Practitioner • Databricks Certified Data Engineer Professional • Databricks Certified Data Analyst Associate / Databricks SQL certification • DAMA CDMP • Azure Data Engineer Associate
Domain Experience-
Commercial and Retail Relevant Knowledge Areas: Enterprise Data Modelling Across Business Functions Cross-Domain Data Integration Source System Harmonization Customer, Product, and Sales Data Models Commercial and Retail Data Architecture
Key Competencies-
Success Measures • Reusable and scalable enterprise data models delivered as per agreed project timelines. • Improved consistency of business definitions, source mappings and consumption models across domains. • Clear traceability from business requirement to logical model, physical model and implementation layer. • Reduced ambiguity in source-to-target mapping and reporting logic. • Data models are aligned to Databricks Lakehouse, Delta Lake and Unity Catalog governance standards. • Positive stakeholder feedback from business, architecture, governance and engineering teams.
Ideal Candidate Profile A senior-level Data Modeler who can act as the modelling authority for a Databricks-based enterprise data platform. The candidate should combine strong Data Vault 2.0 and dimensional modelling expertise with practical understanding of Delta Lake, Unity Catalog, Lakehouse layers, governance, metadata and business-facing requirement translation.
