JOB DESCRIPTION - DB DATA MODELLER
Conceptual | Logical | Physical | Dimensional | Semantic Data Modelling
Experience
10+ years
Role Level
Senior DB Data Modeller
Primary Focus
Enterprise and Analytical Data Models
Domain
Insurance and Benefits Management
Role Summary
Seeking a Senior DB Data Modeller with 10+ years of experience translating complex business requirements into scalable, governed and implementation-ready data models. The role will develop conceptual, logical, physical, dimensional, canonical and semantic models for operational, analytical and AI-ready platforms, supported by Insurance and Benefits Management domain experience.
Key Responsibilities
Create and maintain conceptual, logical and physical data models for transactional databases, data warehouses and Lakehouse platforms.Design normalized relational models and dimensional star/snowflake schemas for operational reporting, analytics, BI and AI use cases.Define canonical models for benefits, employer, client, plan, member, enrollment, policy, premium, claims, renewal and carrier data.Work with business analysts, data owners and architects to identify entities, attributes, relationships, cardinality, keys and business rules.Develop source-to-target mappings, transformation rules and model specifications for ETL/ELT and API integration teams.Perform data profiling and SQL analysis to validate source structures, model assumptions, referential integrity and data quality.Define naming standards, data types, domains, reference data, slowly changing dimensions, effective dating and history patterns.Maintain data dictionaries, model metadata, business definitions, lineage and traceability through governed repositories and catalogues.Conduct model reviews with architects, DBAs, engineers, BI teams and business stakeholders; resolve duplication and semantic conflicts.Support physical implementation, indexing, partitioning, query-performance tuning, model versioning, release impact analysis and production issue resolution.Mandatory Technical Skills
Conceptual, logical and physical data modelling; relational and dimensional modelling; normalization and denormalization; star and snowflake schemas; entity-relationship modelling; canonical and semantic models; master and reference data; data profiling and source-to-target mapping; advanced SQL; PostgreSQL, SQL Server, Oracle or comparable relational databases; data-warehouse and Lakehouse modelling; Delta Lake and Azure Databricks concepts; Power BI semantic models; metadata, lineage and business glossary; data quality and reconciliation; model versioning and change control; ERwin, ER/Studio, SAP PowerDesigner, Visio or comparable modelling tools; Git, Jira or Azure DevOps; Agile delivery practices.
Experience and Qualifications
10+ years of experience in data modelling, database design, data warehousing, data architecture or enterprise data management.Proven experience delivering conceptual, logical and physical models for complex enterprise and analytical solutions.Strong hands-on SQL, data profiling, mapping, dimensional modelling and relational database-design experience.Experience collaborating with business, architecture, database, data-engineering, BI, governance and testing teams.Experience implementing modelling standards, model governance, documentation and design-review controls.Bachelor’s degree in Computer Science, Information Technology, Data Engineering or a related discipline; relevant data-modelling certification is preferred.Domain Experience
Insurance or employee-benefits experience across employer, client, plan, member, eligibility, enrollment, policy, carrier, premium, claims and renewal data.Understanding of sensitive and regulated information, employer-level isolation, effective dating, auditability and governed metric definitions.Experience modelling canonical benefits data, certified metrics and semantic structures for role-based BI or natural-language analytics is advantageous.Core Competencies
Structured data thinking, analytical problem-solving, business-to-technical translation, model precision, documentation discipline, stakeholder facilitation and the ability to balance consistency, performance, scalability, governance and implementation practicality.
