Top Data Engineer Jobs in Toronto

As a Senior DevOps Developer, you will lead initiatives to design, implement, and maintain infrastructure for the data engineering and ML teams while ensuring reliable deployments and collaboratation with various technical teams.

The Senior Data Engineer will develop scalable big data solutions, manage data pipelines, collaborate with teams, and optimize data systems for marketing and data science.

The Software Engineer will design and optimize large-scale data infrastructure, focusing on distributed systems and data processing for generative media models, collaborating closely with research scientists and enhancing multi-modal training pipelines.

Lead design and implement a scalable Medallion lakehouse on GCS and Databricks. Build and govern ingestion and transformation pipelines (Astro/Airflow, PySpark, Delta Live Tables), apply domain-driven data modeling, manage Unity Catalog access and lineage, collaborate with analytics and vendors, and proactively surface risks with clear documentation.

The Data Reliability Engineer will enhance the resilience and scalability of data infrastructure, focusing on automation and reliability. Responsibilities include managing data pipelines, operating Kubernetes clusters, and defining observability standards.

Lead large-scale technical initiatives on the Data Ingestion Team, focusing on building and operating distributed systems that process billions of events daily.

The Principal Software Engineer will lead the integration of AI with a focus on developing data orchestration solutions, modernizing legacy systems, and enhancing web infrastructure for optimal performance.

The Lead Data Engineer is focused on leading application systems analysis and programming while collaborating with management teams for system enhancements, resolving high-impact issues, and providing expertise in applications programming.

Seeking a Senior Data Engineer to develop and maintain data pipelines, collaborating with data science teams to deliver accurate data for business insights.

The Machine Learning Engineer / Data Scientist will build and deploy machine learning solutions, working through the ML lifecycle from problem framing to production.

The role involves designing scalable data pipelines for 3D, video, and sensor data, optimizing infrastructure, and productionizing ML models with researchers.

You will architect and develop new features for SELECT, focusing on cost management, UI components, data visualization, and improving application performance.

The role involves building the data platform, overseeing architecture, improving performance and reliability, and collaborating with various teams. Responsibilities also include technical direction and mentoring other engineers.

The Lead Data Engineer will design and enhance data pipelines and infrastructure for data-driven decisions, mentor engineers, and establish data governance.

The Big Data and SQL Developer is responsible for designing and implementing scalable Big Data solutions, mentoring engineers, and engaging with stakeholders, balancing technical tasks with leadership duties.

Design, build, and maintain scalable Databricks-based ETL/ELT pipelines and data models optimized for AI/ML. Manage orchestration, data quality, governance, and CI/CD (ADO → GitHub) while collaborating with cross-functional teams in Agile delivery.

Design and build scalable, semantically consistent 360 entity data models and transformation pipelines. Implement cleansing, enrichment, KPIs, scoring, and business rules in SQL and Python/Scala, ensure data quality, lineage, and performance at scale. Use AI coding assistants and SDD to accelerate development, create tests and documentation, and collaborate with domain experts, data scientists, and product teams to deliver trusted, reusable data assets.

As a Staff Software Engineer, you'll architect and build foundational platform services, focusing on data infrastructure, healthcare interoperability, and secure APIs while mentoring other engineers.

The Senior Data Software Engineer will design scalable data pipelines, develop backend services, and ensure data quality for AI-powered personalization. Responsibilities include working with diverse teams to translate business needs into robust data systems, ensuring accuracy, reliability, and performance optimization in production environments.

Build and scale AI data and ML infrastructure: evaluation systems, fine-tuning pipelines, agent-first product surfaces, high-throughput data workflows, and integrations of new models into production.

As a Senior Data Engineer, you will build scalable data pipelines, manage cloud infrastructure, and support analytics and data science teams, leveraging various technologies to deliver impactful solutions for clients.

Design, build, and maintain scalable data pipelines and tenant-aware APIs to ingest and transform data from ad platforms, order systems, and real-time events. Ensure reliability, observability, and maintainability across event-driven and batch architectures, implement authentication flows, optimize data workflows in a cloud-native environment, and collaborate with product, infrastructure, and data teams to onboard new sources.

Lead design and implementation of scalable Hadoop/Spark big data platforms and pipelines, optimize cluster performance, enforce data security/governance, mentor engineers, and integrate cloud-native services for analytics and near-real-time ingestion.

As a Photonics Data Analysis Engineer, you will develop software for analyzing and measuring photonic devices, requiring advanced mathematical skills and strong programming expertise, particularly in Python and data visualization.

Triage and resolve Master Data Management (Party, Risk, Reference) issues in Stibo STEP, run targeted data-cleansing sprints, monitor data quality dashboards, support releases/UAT, and maintain documentation and playbooks to prevent recurring data issues.