Senior Data Modeller

Job Title: Senior Data Architect/Modeller

Location: Toronto, ON

Work Model: Hybrid (3 days onsite)

Start Date: November 23, 2026

End Date: April 23, 2027

Allocation: 100%

Position Overview

We are seeking a Senior Data Architect / Modeller to design and evolve enterprise data architecture and data models that enable scalable analytics, AI, and reusable data products. This role will help define the future-state enterprise data architecture, identify critical data assets, and establish standards and integration patterns that ensure enterprise data is governed, reusable, accessible, and AI-ready.

Key Responsibilities

• Design and maintain enterprise data architecture and conceptual, logical, and physical data models supporting AI, analytics, and operational use cases.
• Define scalable architecture patterns for data integration, storage, transformation, sharing, and consumption.
• Design reusable data products and common data models for multiple business and AI use cases.
• Contribute to the future-state enterprise data architecture, including transition from current platforms and architectures.
• Identify and define critical data assets and domains aligned to enterprise priorities.
• Establish and promote enterprise data architecture, modelling, interoperability, and integration standards.
• Partner with Data Governance teams to embed data ownership, metadata, lineage, classification, lifecycle, and data quality requirements.
• Assess data architecture needs for AI, Generative AI, machine learning, advanced analytics, and reporting initiatives.
• Collaborate with business, architecture, engineering, and Data & AI teams to translate requirements into scalable data solutions.
• Identify architecture gaps, duplication, silos, and opportunities for improved data reuse and interoperability.

Must-Have Skills

• Significant experience designing enterprise data architectures and conceptual, logical, and physical data models for AI/ML, Generative AI, analytics, and cloud workloads.
• Strong understanding of data requirements for model training, inference, RAG, feature engineering, and AI/ML pipelines.
• Deep expertise in cloud data platforms, data engineering, integration, APIs, metadata, lineage, and data quality.
• Strong knowledge of the Azure data and AI ecosystem, including Azure Machine Learning and Microsoft Foundry / Azure AI Foundry.
• Strong understanding of data governance, ownership, quality, security, privacy, lineage, and information lifecycle management.