Top Data Engineer Jobs in Toronto

Responsible for bridging enterprise data with AI capabilities, translating business challenges into operational solutions, and driving complex sales deals by connecting technical solutions to business value.

Conduct research and design AI data-security and privacy architectures for endpoint devices. Prototype and build privacy-preserving defenses, analyze emerging attack vectors and regulations, and collaborate with global labs and product teams to transition research into production-grade endpoint security solutions.

Build and maintain high-performance, fault-tolerant distributed backend services and data pipelines for large genomic and health datasets. Design storage, indexing, and async event workflows; optimize latency, throughput, and reliability. Partner with bioinformatics and product teams to validate scientific accuracy and guard AI-driven responses.

Lead architecture and hands-on engineering of Pantheons data platform: extend ingest, curation, and retention services; build ML/LLM-ready data infrastructure; mentor engineers; operate in a DevOps model; ensure data governance, observability, reliability, and cross-functional partnership.

Own and operate backend and data services for a cybersecurity platform: design and run high-volume streaming ingestion, ETL/ELT pipelines, lakehouse/warehouse modeling, APIs, and observability. Mentor engineers, contribute across TypeScript/Python/Go microservices on Kubernetes, ensure performance/scale/security, and evaluate AI tools to accelerate development.

The Senior Data Engineer designs, builds, and maintains data pipelines and ensures data quality controls while supporting data analytics and reporting needs.

The Senior Backend Engineer will design and build scalable backend systems for SaaS products, integrate ML/AI capabilities, and lead technical direction across product areas.

Design, build, and maintain robust terabyte-scale data pipelines and stream-processing applications. Collaborate with product teams, participate in full SDLC, automate tests and deployments, monitor operations, and contribute to technical strategy and continuous improvement. Participate in on-call rotation.

Lead and mentor a team of developers, oversee cloud product delivery, drive engineering practices, and ensure high-quality standards while contributing to cross-functional initiatives.

Build, maintain, and optimize data pipelines and transformations on Snowflake and Azure. Develop DBT models, ingest diverse data sources, monitor pipelines, troubleshoot issues, collaborate on governance, and use GenAI-assisted tools to accelerate development and documentation.

Lead design, development, deployment, and operational support of scalable cloud data platforms and ELT/ETL pipelines on Azure. Build data products for analytics and AI, optimize performance/cost, implement CI/CD and observability, collaborate with stakeholders, and troubleshoot complex data workflows using Snowflake, dbt, Python/PySpark, and orchestration tools.

Design, build, deploy, and own high-performance, scalable data-platform components for streaming analytics, reporting, and ML. Collaborate with cross-functional teams, perform design/code reviews and performance tuning, debug production incidents, participate in on-call rotation, and mentor engineers to scale the organization.

Lead design, architecture, and delivery of enterprise-scale data platforms using Databricks and Spark. Architect Lakehouse solutions, build ETL/ELT pipelines, implement IaC and CI/CD, ensure data governance and security, mentor engineers, and partner with Fortune 500 clients from discovery through production.

Own and improve annotation systems, workflows, and tooling to produce high-quality training data. Build Python and web tools, integrate model-assisted labeling and active learning, implement data quality checks, support dataset versioning and evaluation, and coordinate cross-team efforts to measure and improve annotation throughput and model impact.

Design and execute data quality strategies for enterprise data platforms. Test ETL/data pipelines, perform source-to-target reconciliations, build validation frameworks, write complex SQL for analysis, perform regression testing, root-cause defects, and collaborate with engineers and stakeholders to improve data integrity.

Design and build operational data systems, including pipelines, storage layers, and APIs for near real-time enforcement and insights, balancing streaming and batch paradigms.

The Senior Data Engineer will lead the design and development of scalable data infrastructure for analytics and AI capabilities, ensuring compliant data processing systems and improving the data platform.

As a founding Data Scientist/Machine Learning Engineer, you'll develop AI/ML models, enhance product capability, and drive impactful user outcomes while working closely with product teams.

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

The role involves analyzing software requirements, contributing to software development, mentoring developers, and leading design/code activities in a big data context.

The role requires developing solutions using IBM DataPower, with a focus on creating WSP, XFW, MPGW, and managing certificates and encryption. Candidates should have strong skills in troubleshooting and working with various DataPower devices.

The Senior Data Platform Engineer will design and operate a unified data platform, ensuring reliable data flows and collaborating across teams to shape data strategy and architecture.

Own technical execution of customer data migrations: explore source systems, export data, author declarative version-controlled mappings, transform and validate data, run end-to-end migrations on internal platform, build reusable templates/validation, troubleshoot discrepancies, and document mapping and transformation logic for auditable production cutovers.

Lead and manage applications development teams for Big Data platform delivery. Hands-on development and troubleshooting across Hadoop ecosystem, build/oversee data pipelines (batch and streaming), ensure CI/CD and cloud integration, mentor engineers, manage stakeholders, budgets, and compliance with policies and controls.

Lead hands-on development and delivery of big data platform projects. Manage and mentor engineers, design data flows and cloud/microservice architectures, ensure DevOps/CI-CD practices, mitigate risks, and collaborate with business and product stakeholders to deliver high-quality data products.