Senior Data Modeler – Databricks Lakehouse

Senior Data Modeler – Databricks Lakehouse

Enterprise Data Platform | Data Vault 2.0 | Delta Lake | Unity Catalog

Role Level: Senior / Lead Individual Contributor

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

1. 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.
2. 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.
3. 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.
4. 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.
5. 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

1. Data Modelling Expected Level: Expert Skills: Data Vault 2.0 Dimensional Modelling Conceptual Data Modelling Logical Data Modelling Physical Data Modelling Canonical Data Models Data Warehousing

2. Databricks Lakehouse Expected Level: Advanced to Expert Skills: Delta Lake Medallion Architecture Databricks SQL Unity Catalog Lakehouse Modelling Patterns

3. SQL and Data Analysis Expected Level: Advanced Skills: Advanced SQL Query Optimization Complex Joins Common Table Expressions (CTEs) Set Operations Data Profiling Data Validation

4. Data Engineering Understanding Expected Level: Intermediate to Advanced Skills: ETL Concepts ELT Concepts Spark Fundamentals PySpark Fundamentals Data Transformation Design Pipeline-Oriented Thinking

5. Governance and Metadata Expected Level: Advanced Skills: Data Lineage Business Glossary Management Data Dictionary Creation and Maintenance Data Ownership Frameworks Data Quality Rules Audit-Ready Documentation

6. Tools Expected Level: Advanced Skills: ERwin SQLDBM ER Studio Microsoft Visio Draw.io Equivalent Data Modelling Tools

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-

1. Insurance- Relevant Knowledge Areas: Policy Administration Claims Management Customer Data and Processes Premium Calculation and Processing Commission Management Distribution Channels Finance and Accounting Concepts Reinsurance Concepts

2. Regulatory Reporting- Relevant Knowledge Areas: Regulatory Reporting Frameworks End-to-End Data Traceability Source-to-Report Mapping Mapping Logic Documentation Data Quality Controls Audit Evidence and Compliance Support

3. 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

4. Analytics and Business Intelligence (BI) Relevant Knowledge Areas: Gold Layer Design Semantic Data Models Power BI Data Modelling Executive and Management Dashboards Self-Service Analytics Reporting and Visualization Frameworks

Key Competencies-

1. Data Vault 2.0 and Enterprise Data Modelling Expected Level: Expert

2. Databricks Lakehouse and Delta Lake Modelling Expected Level: Advanced to Expert

3. Business Requirement Translation Expected Level: Advanced

4. Data Governance, Metadata and Lineage Expected Level: Advanced

5. SQL and Data Analysis Expected Level: Advanced

6. Stakeholder Management Expected Level: Advanced

7. Architecture Review and Design Quality Expected Level: Advanced

8. Mentoring and Technical Leadership Expected Level: Advanced

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.