Data Engineer – Canadian Tire – Toronto, ON
Location: Toronto, ON | Company: Canadian Tire
Canadian Tire Corporation is looking for a Data Engineer to join its Advanced Analytics team in Toronto, Ontario. This is a technically rich role focused on bridging the gap between data science and production-ready AI systems — if you thrive in cloud-based ML environments and love building pipelines that actually work at scale, this one’s worth a close look.
Day-to-day, you’ll be deploying and managing machine learning models in production, building ETL data pipelines, and working alongside data scientists and IT teams to ensure AI solutions are stable, reliable, and performing as expected. The work touches everything from Azure Databricks and Azure Data Factory to containerization and CI/CD pipelines.
About the Role: Data Engineer
As a Data Engineer on the Advanced Analytics team, your core mandate is ensuring that ML models run smoothly in production environments. You’ll design and manage robust ETL pipelines, maintain a curated feature store, and automate model evaluation processes. Your contributions directly influence the quality and reliability of AI-driven decisions across the business.
You’ll collaborate closely with both data scientists and external data engineering teams to align infrastructure with deployment needs while owning the ML model lifecycle internally. Strong communication and the ability to translate between technical and business concepts will be just as important as your coding skills.
Benefits and Salary
The typical hiring range for this position is $64,000 to $106,000 per year, with final compensation based on experience, skills, and market conditions. Canadian Tire also offers a comprehensive benefits package that includes retirement programs, performance incentives, continuing education programs, mental health benefits of $5,000 per year for eligible employees and their families, career growth opportunities, product discounts, and Canadian Tire Profit Sharing for eligible employees.
Job Details
📌 Job Type: Permanent
🏢 Company: Canadian Tire Corporation
📍 Location: Toronto, ON
🆔 Requisition ID: JR154947
💰 Pay: $64,000 – $106,000 per year
Responsibilities
In this role, you’ll be hands-on across the full ML model lifecycle — from ingesting and transforming raw data to deploying models and monitoring their real-world performance. Each of the following responsibilities contributes to building a dependable, scalable AI infrastructure for one of Canada’s largest retailers.
- Design and manage ETL data pipelines that extract, transform, and load data from diverse sources with precision
- Implement and maintain robust data quality checks and a curated feature store
- Deploy and scale existing machine learning models in production, ensuring reliable data ingestion, updates, and model scoring
- Collaborate with data scientists to provide clean, well-structured data for modelling and automate experimental models for production use
- Coordinate with external data engineering teams for infrastructure setup and database management
- Own the ML model lifecycle internally while aligning with external teams on infrastructure and deployment
- Develop monitoring systems and automate processes for continuous model evaluation
Requirements / Skills
The ideal candidate brings a strong foundation in cloud-based data engineering combined with practical experience deploying AI models. Canadian Tire values problem-solvers who are as comfortable presenting to stakeholders as they are writing Python scripts — adaptability and continuous learning are key traits for this team.
- Master’s degree in Computer Science, Software Engineering, or an equally technical field
- 2+ years of data engineering experience with Azure-related technologies (Databricks, Data Factory, Data Lake Storage, Synapse)
- Hands-on proficiency with Python, PySpark, MLflow, and Azure ETL automation scripting
- Experience with model deployment frameworks (e.g., TensorFlow Serving), containerization (Docker, Kubernetes), and cloud platforms (Azure)
- Familiarity with DevOps/CI-CD practices using tools like GitLab or Jenkins for AI model deployment
- Solid understanding of Azure infrastructure including RBAC, Azure AD integration, security principles, and credential management
- Strong communication skills — able to lead technical presentations, workshops, and design sessions for both technical and business audiences
- Preferred: Experience with data modelling, data lakehouse architecture, delta lake, Apache Spark, or deep learning frameworks like PyTorch
How to Apply
To apply for this Data Engineer position at Canadian Tire in Toronto, use the official link below. Make sure your resume is up to date and reflects your relevant Azure and ML deployment experience before submitting.
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Job Summary & Tips for Applying
Quick Summary & What to Highlight: This Data Engineer role at Canadian Tire in Toronto is perfect for candidates who excel in ML model deployment, ETL pipeline development, and Azure cloud technologies. On your resume, emphasize any experience with Azure Databricks, PySpark, and MLflow, attention to data quality, and your ability to work in a fast-paced, cross-functional environment. If you’ve previously worked in data engineering or ML operations (MLOps), make sure to highlight specific achievements and responsibilities that align with production model management and automation.
Resume & Application Tips: Before applying, tailor your resume to match the job description. Include keywords like ETL pipelines, Azure Data Factory, and ML model productionisation that appear in the posting. Quantify your achievements where possible (e.g., “reduced model deployment time by 40%” or “managed ETL pipelines processing 10M+ records daily”). Write a brief cover letter expressing your genuine interest in Canadian Tire and why you’re excited about contributing to their Advanced Analytics team in Toronto. Double-check your application for spelling errors and ensure your contact information is current.
Interview Preparation: If selected for an interview, research Canadian Tire‘s values, innovation initiatives, and company culture beforehand. Prepare specific examples using the STAR method (Situation, Task, Action, Result) to demonstrate your data engineering and MLOps skills. Common questions may include scenarios about handling pipeline failures, scaling models in production, and cross-team collaboration. Dress appropriately for a corporate technology environment, arrive 10–15 minutes early, and bring copies of your resume. Prepare thoughtful questions about the team’s tech stack, data architecture roadmap, and growth opportunities. After the interview, send a thank-you email within 24 hours reiterating your interest in the position.