RQ09732 - Sr. Data Architect/Modeller

RQ09732 - Sr. Data Architect/Modeller

Downtown, Toronto (Jarvis St.)

Hybrid: 3 Days onsite / 2 days remote

- From October 20, 2025, the candidate is required to work onsite 4 days a week and 1 day from home

- From January 5, 2026, the candidate is required to work onsite 5 days a week fully

Contract 6+ months, possible extension

Responsibilities:

• Develops and implements the data architecture for application development in a complex and distributed environment, including the determination of the flow and distribution of data, the location of databases, and data access methods
• Provides a standard common business vocabulary, expresses strategic data requirements, outlines high level integrated designs to meet these requirements, and aligns with the enterprise strategy and related business architecture
• Define conceptual, logical model and physical model mapping from data source to curated model and data mart.
• Design dimensional data mart models, create source-to-target-mapping documentation, design and document data transformation from curated model to data mart
• The Architect / Modeler must have previous work experience in conducting Knowledge Transfer and training sessions, ensuring the resources will receive the required knowledge to support the system. The resource must develop learning activities using review-watch-do methodology & demonstrate the ability to prepare and present.
• Development of documentation and materials as part of a review and knowledge transfer to other members
• Monitor identified milestones and submission of status reports to ensure Knowledge Transfer is fully completed

Must-Have:

• Proficiency in SQL and python, with hands-on experience using Databricks and Spark SQL for data modeling and transformation tasks.
• Experience with at least two different platforms, operating systems, environments, database technologies, languages and communications protocols.
• Experience in design, development and implementation of data models for analytics and business Intelligence
• Knowledgeable in BI modelling methodologies (Inmon, Kimball, data vault), data mapping, data warehouse, data lake and data lakehouse for enterprise.
• Strong understanding of data quality principles, with the ability to design and implement automated data quality checks using tools such as Python and SQL, ensuring data integrity across pipelines and models.
• Experience with middleware and gateways
• Experience in designing/developing an automated data distribution mechanism