Our client is looking for a Senior Data Modeler with Azure Databricks, MS Fabric and Python experience to design and evolve dimensional data models for power reporting and self-serve analytics
This role requires you to be onsite 3 days a week in Calgary or Edmonton
Must Haves :
- 7+ years' experience working within Data Analytics preferably in the following areas : data modeling, analytics engineering and / or BI modeling
- Experience with SQL for data profiling, reconciliation, and validation.
- Experience with Power BI semantic modeling (tabular concepts, metrics governance, deployment patterns).
- Expert-level capability in dimensional modeling ( Kimball ) including :
- Star / snowflake schema design, fact table patterns, conformed dimensions
- SCD Type 1 / 2 (and when to use each), role-playing dimensions, bridge tables
- Additive vs semi-additive measures, snapshots vs transaction facts, late-arriving dimensions
- Experience with governance and security concepts (cataloging, lineage, RBAC / ABAC, auditing, data classification).
Key responsibilities
Enforce the design and evolution of dimensional models (facts, conformed dimensions, metrics) that support enterprise analytics and self-serve use cases.Lead discovery with business stakeholders to define business processes, grain, measures, hierarchies, and conformed dimensions, translating these into model specifications and semantic definitions.Define and enforce modeling standards (naming conventions, metric definitions, dimensional patterns, SCD strategies, documentation templates).Partner with data engineering to ensure performant implementations on Azure platforms (e.g., curated layers, incremental patterns, scalability, and query performance).Drive conformed dimension strategy and cross-domain consistency.Establish and maintain a metrics and dimensional standards library, promoting consistent definitions across reports and data products.Support semantic and consumption layers to ensure governed, consistent results for business users.Define data quality and reconciliation approaches (balancing, exception handling, completeness / accuracy rules) and partner with teams to operationalize them.Contribute to governance practices including metadata, lineage, classifications (e.g., PII), and access patterns aligned to enterprise controls.Produce and maintain high-quality modeling artifacts : conceptual / logical / physical designs, model diagrams, data dictionaries, mapping documents, and change logs.Provide technical leadership through design reviews, peer coaching, and mentorship for engineers / analysts building or extending models.