Interim Senior Data Modeler

Go Fractional; Richmond Hill; Hybrid; CAD 83,000 - 110,000; Full time

Job summary

Go Fractional in Richmond Hill is seeking a Senior Data Modeler for a 6‑month contract to design enterprise analytical data models supporting reporting, self‑service analytics, planning, and data science.

You will work with data engineers, BI developers, analysts, and governance partners to build conformed dimensions, star schemas, and governance‑ready documentation, prioritizing reusable patterns and clear lineage.

Qualifications

• 7+ years of experience in data warehousing, analytics engineering, or data architecture.
• Experience designing dimensional warehouses using Kimball methodologies.
• Strong SQL skills and ability to profile data and test models.
• Experience with dbt or similar SQL-based transformation tools.
• Experience modeling data for reporting, BI, and self-service analytics.

Responsibilities

• Design conceptual, logical, and physical analytical data models across major business areas.
• Define grain of fact tables and design measures, dimensions, hierarchies, keys, and relationships.
• Build conformed dimensions and reusable business entities for consistent analysis.
• Abstract source structures into intuitive analytical models while preserving business logic.
• Develop dimensional models using star schemas and slowly changing dimensions.
• Collaborate with stakeholders to define requirements, definitions, and detail levels.
• Document models with lineage, naming standards, and governance considerations.
• Lead design reviews and mentoring to ensure reusable, maintainable models.

Skills

SQL proficiency

Dimensional modeling

Kimball methodology

Stakeholder collaboration

Analytical thinking

Education

Bachelor’s degree

Master’s degree

Tools

dbt

Snowflake

Job description

Senior Data Modeler – 6 Months Contract

REPORTS TO: Manager, Digital Analytics

LOCATION: Hybrid / Corporate Office in Richmond Hill, Ontario

SALARY: $60/h - $80/h

About the Role

We are seeking a senior data modelling specialist to design and evolve the enterprise analytical data model that supports reporting, self-service analytics, planning, and data science. You will translate complex operational data into durable, business-centered structures that make measures, dimensions, and relationships clear and reusable across analytical use cases.

This role sits within Data Engineering and works closely with business stakeholders, analysts, BI developers, data engineers, and data governance partners. The primary focus is dimensional modelling and analytical data architecture, not pipeline orchestration or cloud infrastructure engineering.

Key Responsibilities

• Design conceptual, logical, and physical analytical data models across major business subject areas.
• Define the grain of fact tables; design measures, dimensions, hierarchies, keys, and relationships; and select appropriate patterns for historical change.
• Build conformed dimensions and reusable business entities that support consistent analysis across source systems and functional domains.
• Abstract transactional and operational source structures into intuitive analytical models rather than reproducing source-system schemas.
• Develop dimensional models using Kimball-style techniques, including star schemas, role-playing dimensions, bridge tables, factless fact tables, and slowly changing dimensions.
• Partner with business stakeholders and analysts to understand analytical questions, reporting workflows, definitions, and required levels of detail.
• Define business meaning, calculation intent, lineage, naming standards, and model documentation so that analytical assets are understandable and governed.
• Implement models using DBT and validate that delivered structures preserve the intended grain and business logic.
• Review existing warehouse structures, identify duplication or tightly coupled designs, and guide their evolution towards reusable and supportable analytical models.
• Provide modelling leadership through design reviews, standards, mentoring, and constructive challenge.

What Success Looks Like

• Analysts can answer new questions by combining well-defined facts and conformed dimensions without repeatedly rebuilding business logic.
• Different reports and subject areas use consistent definitions for shared business concepts.
• Models are stable enough to absorb source-system change while remaining clear to analytical consumers.
• Fact table grain, history, relationships, and calculation rules are explicit, tested, and documented.
• Data engineering pipelines implement governed model designs instead of exposing operational structures directly to reporting tools.

Qualifications

• 7+ years of experience in data warehousing, analytics engineering, business intelligence, data architecture, or a related discipline, with substantial hands‑on responsibility for analytical data modelling.
• Demonstrated experience designing dimensional data warehouses using Kimball methodologies.
• Deep understanding of dimensional modelling concepts, including fact table grain, conformed dimensions, slowly changing dimensions, surrogate keys, hierarchies, additive and non‑additive measures, and many‑to‑many relationships.
• Experience creating conceptual, logical, and physical models that abstract data from multiple transactional source systems.
• Strong SQL skills and the ability to profile source data, validate assumptions, and test whether a model represents business processes correctly.
• Working knowledge of dbt or similar SQL-based transformation frameworks.
• Experience modelling data for reporting, BI, semantic models, and self-service analytics.
• Ability to facilitate requirements and model‑design discussions with both business and technical participants.
• Ability to explain modelling choices, trade‑offs, and constraints clearly through diagrams, definitions, examples, and design documentation.
• Experience reviewing implemented models for structural quality, usability, consistency, and alignment with modelling standards.
• Experience modelling data across multiple enterprise applications or integrating similar business concepts from different source systems.
• Familiarity with medallion‑style data platform architecture and the role of curated, business‑oriented models within that architecture.
• Exposure to Git and automated deployment practices for database and transformation changes.

Preferred Qualifications

• Experience implementing analytical models in Snowflake or a comparable cloud data platform.
• Experience supporting Power BI or another enterprise BI platform, including collaboration on semantic layer design.
• Experience with data governance, metadata management, lineage, data quality rules, and business glossaries.

Education

• Bachelor's or master's degree in computer science, information systems, engineering, mathematics, business analytics, or a related field, or equivalent practical experience.

Work Authorization Requirement

Applicants must be legally authorized to work in Canada at the time of application and throughout employment. The company does not provide visa sponsorship for this role.