JOB SUMMARY We are seeking an experienced Senior Data Modeler with strong expertise in enterprise data modeling and insurance domain data. The successful candidate will design, develop, and maintain logical and physical data models that support analytics, reporting, actuarial, underwriting, broker, and enterprise data initiatives. The role requires close collaboration with business analysts, data analysts, data engineers, architects, actuaries, underwriters, and reporting teams. The ideal candidate will be skilled at translating business requirements into scalable data structures, defining source-to-target mappings, and ensuring that data models align with enterprise standards, governance frameworks, and reporting needs. Experience with insurance, underwriting, or broker data, along with familiarity with Databricks and Azure data platforms, is highly desirable.
Key Responsibilities Design, develop, and maintain logical and physical data models that support analytics, reporting, actuarial, underwriting, broker, and enterprise data initiatives.
Translate business requirements into scalable data structures.
Define source-to-target mappings.
Ensure data models align with enterprise standards, governance frameworks, and reporting needs.
Required Qualifications Minimum 12+ years of relevant experience.
Strong expertise in conceptual, logical, and physical data modeling.
Hands-on experience designing enterprise data models for data warehouses, data lakes, lakehouses, and reporting platforms.
Advanced SQL skills.
Recent insurance domain experience.
Good knowledge of Actuarial data models.
Familiarity with Azure Data Platform (Databricks, Azure Data Lake, Synapse, Azure SQL).




Proven experience as a Senior Data Modeler, Enterprise Data Modeler, Data Architect, or similar role.
Solid knowledge of: Conceptual, logical, and physical data modeling, Enterprise data modeling, Dimensional modeling, Star and snowflake schemas, Fact and dimension design, Data grain and aggregation, Primary and foreign keys, Relationships, hierarchies, and slowly changing dimensions, Normalization and denormalization, Reference and master data modeling.
Experience designing models for data warehouses, data marts, data lakes, lakehouses, and reporting platforms.
Strong understanding of analytical and operational data structures.
Ability to design scalable models that support reporting, analytics, actuarial analysis, and downstream data products.
Experience defining reusable enterprise entities and canonical data structures.
Strong SQL skills, including data investigation, profiling, validation, joins, aggregations, and relationship analysis.
Experience creating detailed source-to-target mappings and transformation specifications.
Familiarity with ETL/ELT processes, data integration patterns, and pipeline dependencies.
Experience with relational databases, data warehouses, and cloud data platforms.
Familiarity with Databricks, Delta Lake, Azure Data Lake, Azure Synapse, Azure SQL, or related Azure data services.




Understanding of performance considerations for large-scale data models, including partitioning, indexing, clustering, distribution, and query optimization.
Experience working with structured and semi-structured data.
Understanding of data lineage, metadata repositories, data catalogs, and data dictionaries.
Experience with metadata management and data governance frameworks.
Knowledge of: Business and technical metadata, Data lineage, Data dictionaries, Data ownership and stewardship, Data quality rules, Critical data elements, Data classification, Data standards and naming conventions.
Ability to align data models with enterprise governance, security, privacy, and compliance requirements.
Experience supporting consistent definitions and governed reporting across business functions.
Preferred Qualifications Experience working in the insurance industry, particularly in areas such as: Underwriting, Broking, Policy administration, Premium and commission processing, Claims, Risk and exposure, Actuarial reporting, Portfolio and performance analysis.
Strong understanding of insurance data entities and relationships, including policies, risks, insured parties, brokers, submissions, quotes, bound business, premiums, claims, exposures, and financial transactions.
Ability to model complex insurance and underwriting business processes.
Familiarity with insurance data definitions, reporting requirements, business rules, and key performance measures.
Experience supporting actuarial, underwriting, broker, finance, or regulatory reporting is advantageous.

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📌 Senior Data Modeler (Ontario)
🏢 Compunnel
📍 Ontario