Data Engineer

Requisition ID: 188640
Career Group: Corporate Office Careers
Job Category: Engineering Machine Learning Operations
Travel Requirements: 0 - 10%
Job Type: Full-Time

Country: Canada (CA)
Province: Ontario
City: Toronto
Location: Sobeys Innovation Hub

Embark on a rewarding career with Sobeys Inc., celebrated among Canada’s Top 100 employers, where your talents contribute to our commitment to excellence and community impact.

Our family of 128,000 employees and franchise affiliates share a collective passion for delivering exceptional shopping experiences and amazing food to all our customers. Our mission is to nurture the things that make life better – great experiences, families, communities, and our employees. We are a family nurturing families.

A proudly Canadian company, we started in a small town in Nova Scotia but we are now in communities of all sizes across this great country. With over 1,600 stores in all 10 provinces, you may know us as Sobeys, Safeway, IGA, Foodland, FreshCo, Thrifty Foods, Lawtons Drug Stores or another of our great banners but we are all one extended family.

Job title - Data Engineering Analyst

Location: Sobeys COLAB Office (Toronto Downtown)

About Us

The Advanced Analytics team at Sobeys operates on the frontlines of retail innovation. We're a multi-disciplinary group of data scientists, engineers, developers, and analysts building scalable ML/AI solutions that transform how Sobeys and its family of banners engage with customers and run the business. Leveraging the latest cloud technologies and advanced analytics, we uncover deep insights that drive customer engagement, operational efficiency, and business growth—impacting the lives of millions of Canadian shoppers every day.

Your Role:

As a Data Engineer, you will play a hands-on role in developing and maintaining robust, scalable, and efficient data pipelines that power our machine learning and advanced analytics efforts. You'll collaborate with other engineers, data scientists, and analysts to build data infrastructure that ensures the integrity, accessibility, and performance of our data assets.

What You’ll Be Doing:

• Maintain and enhance existing ETL/ELT pipelines (batch and streaming) to process billions of records daily.
• Implement data solutions using Databricks, Snowflake, Spark, Airflow, and other Azure cloud services.
• Collaborate with Analytics, Product, and Data Science teams to support model deployment, data sourcing, and feature engineering.
• Assist in the design and cost sensitive optimization of data models and pipeline structures to meet evolving business and ML requirements.
• Build data workflows with CI/CD pipelines and monitor system health and performance.
• Contribute to data quality, validation, and lineage tracking for reliable downstream analytics and reporting.
• Investigate and troubleshoot data issues, working closely with business and technical stakeholders.

What you have to offer:

What You Bring:

• Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field—or equivalent practical experience.
• 5–8 years of experience in data engineering, preferably in a cloud-based environment.
• Proficiency in SQL and Python - Object Oriented, with hands-on experience in Spark and data pipeline development.
• Experience working with Databricks, Snowflake, Airflow, Azure, and Data Ops Implementation, CI/CD tools like Azure DevOps or Jenkins.
• Comfortable in Linux environments and able to debug and optimize distributed systems.
• Familiarity with data governance principles, access controls, and best practices in data architecture.
• Strong analytical and problem-solving skills, with the ability to work in a fast-paced, collaborative environment.

Nice to Have:

• Experience supporting or integrating Generative AI (GenAI) projects (e.g., embedding pipelines, vector databases, or LLM inference pipelines).
• Exposure to real-time analytics or streaming data pipelines (e.g., Kafka, Delta Live Tables).
• Familiarity with Unity Catalog for data governance in Databricks.
• Experience with MLOps practices or tools (e.g., MLflow, Feature Store).
• Background in retail, e-commerce, or consumer data analytics.

What Success Looks Like:

• Stable and efficient pipelines that deliver timely, high-quality data.
• Well-documented and scalable infrastructure that supports multiple analytics and ML use cases.
• Improved automation and reduced manual data engineering effort.
• Strong cross-functional collaboration and clear communication with stakeholders.
  

At Sobeys we require our teammates to have the ability to adhere to a hybrid work model that requires your presence at one of our office locations at least three days per week. This requirement is integral to our commitment to team collaboration and the overall success of our office culture.

We offer a comprehensive Total Rewards package, which varies by role and designed to help our teammates to live better – physically, financially and emotionally.

We are committed to accommodating applicants with disabilities throughout the hiring process and will work with applicants requesting accommodation at any stage of this process.