Top Data Engineer Jobs in Toronto

The Principal Software Data Engineer will design, develop, and maintain data platforms and pipelines, mentor teams, and drive technical excellence in data solutions, focusing on microservices architecture and scalability.

The Staff Software Engineer will lead the development of backend services and data pipelines for real estate MLS data, mentor teams, and work on AI integration.

The Data Engineer will develop and manage ETL data pipelines, deploy and manage ML models, and automate processes while collaborating with data scientists and IT teams.

The Data Engineer will manage ETL processes, deploy ML models, ensure data reliability, and collaborate with data scientists and IT teams.

The Data Engineer will design and manage ETL pipelines, deploy ML models, collaborate with data scientists, and ensure data quality and model performance.

The Data Engineer will manage ETL pipelines, deploy ML models, monitor performance, and collaborate with data scientists on data solutions.

Develop and optimize data pipelines for reports and analytics, ensuring data quality and compliance with standards while collaborating with engineers for CI/CD processes.

The Data Engineer will manage the full data stack, design database architecture, build ETL pipelines, create data integrations, and oversee reporting and data quality.

The Data Engineer is responsible for designing and optimizing data pipelines, ensuring data integration, and collaborating with teams on AI-driven applications.

The Data Engineer will design and maintain ETL pipelines, work with Snowflake for data warehousing, ensure data quality, and collaborate on analytics initiatives.

As a Data Engineer at ODAIA, you'll design and implement scalable data solutions, manage customer data integration, and support analytics capabilities while collaborating with cross-functional teams.

The Data Engineer II will design and scale a modern data platform, focusing on building scalable data pipelines, support AI use cases, and ensure data quality.

The Data Operations Engineer will ensure the stability and performance of the data platform, manage data pipelines, monitor incidents, and optimize data quality and infrastructure.

As a Principal Consultant, you will lead client engagements in data engineering, manage projects, mentor junior engineers, and implement data solutions using Databricks, AWS, and Azure.

Looking for a Data Engineer experienced in Dataiku to design and maintain data pipelines and infrastructure, ensuring data flow and integration across teams.

This role involves developing and maintaining data pipelines, collaborating with various teams, and leveraging AI for data-driven insights to support business operations.

As a Senior Software Engineer in Telematics, you will develop backend systems, lead projects, and enhance data insights for safety innovations at Lyft.

The Data Engineer will analyze large datasets, build dashboards, optimize SQL queries, and support data infrastructure using various tools and technologies.

The Data Engineer will build and maintain reliable data pipelines and systems for financial forecasting, ensuring data quality and working with stakeholders to support analysis needs.

Design, implement, and own scalable end-to-end data pipelines and infrastructure for cloud cost, usage, and emissions data. Partner with cross-functional teams to produce analytics-ready datasets, ensure data quality and observability, set technical standards, and mentor engineers to drive cost and carbon intelligence for strategic reporting and optimization.

As a Senior Fullstack Engineer, you'll develop and maintain core data browsing features, collaborate with teams, and mentor engineers while contributing to the Sentry stack.

The Senior Data Developer will design and maintain pipelines, optimize ML models, and support scheduling intelligence for healthcare appointments.

Join an AI-driven research project as a Frontend or Fullstack Developer, designing user-friendly features and delivering polished interfaces.

Lead technology initiatives in data lifecycle management, drive technology vision and architecture, and serve as a mentor within Equinix and the industry.

Responsible for developing infrastructure for ML models, data pipelines, leading teams, ensuring compliance, and driving continuous improvement in data engineering practices.