Senior Data Modeler – Databricks Lakehouse

Databricks Lakehouse Modeling

• Define modeling patterns for Bronze, Silver, and Gold layers in Databricks Lakehouse architecture.
• Design structures optimized for Delta Lake storage, Databricks SQL consumption, and scalable analytical workloads.
• Collaborate with architects and data engineers on implementable, performant, and reusable models.
• Support partitioning, clustering, normalization, and denormalization decisions based on use cases.
• Drive standards for transformation logic, model ownership, and consumption readiness.

Governance and Metadata

• Define modeling standards, naming conventions, and data definition guidelines.
• Support Unity Catalog governance through catalog, schema, and table organization principles.
• Maintain data dictionaries, business glossaries, metadata repositories, and model documentation.
• Establish lineage, ownership, quality rules, and auditability with governance and architecture teams.
• Ensure models support regulatory, privacy, security, and compliance expectations.

Stakeholder Engagement and Leadership

• Work with business SMEs, product owners, reporting teams, data engineers, and architects.
• Translate business requirements, reporting needs, and regulatory definitions into structured models.
• Conduct model walkthroughs, design reviews, and stakeholder sign-off discussions.
• Identify gaps in source systems, business logic, reference data, and mapping documentation.
• Mentor junior data modelers and engineers, review deliverables, and represent modeling in architecture forums.
• Create reusable mapping, dictionary, and model review templates and enable end-to-end traceability.

Mandatory Technical Skills

• Expert Data Vault 2.0, conceptual, logical, physical, canonical, and dimensional modeling skills.
• Advanced to expert Databricks Lakehouse, Delta Lake, Medallion Architecture, Databricks SQL, and Unity Catalog skills.
• Advanced SQL, query optimization, complex joins, common table expressions, set operations, data profiling, and data validation.
• Intermediate to advanced ETL, ELT, Spark, PySpark, data transformation, and pipeline-oriented design knowledge.
• Advanced data lineage, business glossary, data dictionary, ownership framework, data quality, and audit-ready documentation skills.
• Advanced experience with ERwin, SQLDBM, ER Studio, Microsoft Visio, Draw.io, or equivalent data modeling tools.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Information Technology, Data Engineering, Data Science, Analytics, or a related discipline.
• At least 10 years of experience in data modeling, data architecture, data engineering, or enterprise data management.
• Hands-on Data Vault 2.0 and dimensional modeling experience for enterprise analytical platforms.
• Experience with cloud-based data platforms, preferably Databricks Lakehouse.
• Strong communication skills for engaging business, technical, and governance stakeholders.

Preferred Skills and Certifications

• Azure Data Lake Storage working knowledge is preferred.
• Power BI semantic modeling awareness is preferred.
• Master Data Management, Reference Data Management, data quality framework, and validation rule experience are preferred.
• Insurance, regulatory reporting, commercial, retail, analytics, or business intelligence domain exposure is preferred.
• Preferred certifications include Certified Data Vault 2.0 Practitioner, Databricks certifications, DAMA CDMP, and Azure Data Engineer Associate.

Company and Hiring Process