Top Data Engineer Jobs in Toronto

Design, develop, and maintain scalable Databricks-based ETL/ELT data pipelines and transformations. Support migration from HANA to a modern lakehouse, ensure data quality and performance, troubleshoot issues, participate in code reviews, and collaborate with architects, analysts, and stakeholders to deliver enterprise reporting, analytics, and AI-ready data solutions.

Design and lead enterprise data architecture for agentic AI, including hybrid lakehouse and graph/vector integrations. Define schemas and knowledge graphs, ensure database performance and governance across Snowflake/Kinetica/BigQuery, establish ETL/ELT and CDC/streaming strategies, and act as technical client lead for architecture, security, and performance benchmarks.

Build and maintain production-grade data processing systems and foundational infrastructure for enterprise AI. Collaborate with researchers and engineers, deliver end-to-end implementations, transform unstructured data into performant datasets, and use distributed processing frameworks and modern analytics tooling to inform products and strategy.

Design, build, and maintain high-performance data pipelines on Snowflake using dbt and AWS. Own code (SQL, Python, Shell, Terraform), ensure data quality and governance, troubleshoot complex issues, lead agile delivery and mentor engineers, collaborate with architects and QA, and produce technical documentation.

Design, develop, test, and support data and AI products. Partner with stakeholders to translate requirements, build ETL/ELT pipelines, perform data modeling/profiling, automate frameworks, troubleshoot production issues, and participate in process improvements.

The AI-Native Data Engineer will build data pipelines, architect databases, design GCP infrastructure, and automate workflows for energy management using AI.

Design, build, and maintain scalable data ingestion pipelines, databases and data lakes for credit and lending datasets. Ensure accurate data mappings, monitoring and alerting, translate stakeholder requirements into analytical data flows, and integrate APIs to deliver reliable, high-quality data products.

The Data Engineer will provide technical support and deployment expertise, review data designs, and ensure code quality while collaborating with international teams on a strategic data initiative.

Build and operate ML training and serving infrastructure: design, develop, and launch backend systems, support availability and on-call, collaborate cross-functionally, and improve developer velocity.

Design, build, and maintain reliable data pipelines and lakehouse assets for batch and streaming workloads. Implement ETL/ELT in Python and SQL, orchestration and monitoring (Airflow/Kubernetes), and retrieval/RAG/vector search pipelines for LLM and AI applications. Ensure data quality, lineage, versioning (Delta/Parquet/Iceberg), cost/performance optimization, and collaborate with MLOps and sports data teams.

Design and implement secure, resumable cloud pipelines to ingest multi-terabyte medical imaging data; build GPU-accelerated orchestration with strong retry semantics and cost controls; define data lifecycle and retention; implement HIPAA/PHI security and compliance controls; and deliver operational tooling (monitoring, alerting, runbooks) for a device fleet.

Lead design and operation of Block's real-time, event-driven data systems. Build and run high-throughput streaming pipelines, turn Kafka event streams into low-latency data products, improve reliability and observability, mentor engineers, and define platform architecture and standards for projections, third-party data proxy, and graph data capabilities.

The Data Engineer will build and manage scalable data pipelines and databases, collaborate on architecture, and drive data systems towards real-time capabilities in a fast-paced, innovative environment.

Design, develop, test, and support enterprise ETL/ELT data integration and pipeline solutions using Talend and relational databases. Build mappings, transformations, reconciliation, and reusable components; optimize SQL; administer Talend tooling; integrate REST APIs via Azure API Management; monitor and troubleshoot production pipelines; support data quality, governance, metadata, lineage, security, and SDLC processes while collaborating with stakeholders and using AI/automation tools for productivity.

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.

Design, build, and maintain SparkSQL/PySpark data pipelines in the central data lake to ingest IoT, product, and unstructured data (video/audio). Produce reliable computed tables for analytics, model training, and dashboards; integrate external datasets; ensure high data quality and uptime; and collaborate with Data Science, ML, and cross-functional teams.

Design, build, and operate scalable data ingestion, replication, and lakehouse infrastructure to move petabytes of data into a Delta Lake on S3. Improve reliability, observability, security, and developer experience for Spark/Databricks processing. Develop internal libraries and tooling (Go/Python), collaborate with cross-functional teams, and help shape long-term data platform and AI-ready infrastructure.

Design, build, and operate large-scale Spark/PySpark data pipelines and the enterprise data platform. Implement data integrations, transformations (Python/SQL), monitoring, and data quality. Build MCP servers and AI-enabled tooling, optimize performance, mentor engineers, and lead cross-functional data projects to enable analytics and automation.

The Senior Data Platform Engineer at 1Password will design, build, and maintain data systems and streaming architectures, ensuring data quality and governance for real-time access.

Own, build, and optimize Spark-based production data pipelines and ML data lake on GCP Dataproc to support recommender systems. Collaborate with ML engineers, resolve performance bottlenecks, and deliver scalable, reliable data products for AI-driven personalization.

The Senior Data Engineer will design and maintain streaming data pipelines, develop analytical databases, and ensure data system reliability while collaborating with teams across the organization.

Build and maintain scalable, reliable ML data platform systems to support dataset generation, model training, analytics, monitoring, and large-scale inference. Collaborate with ML, software, and infrastructure engineers to design cost-efficient data lake and training infrastructure using vendor and open-source tools to enable next-generation ML models.

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

Build and own a versioned, documented gold data layer from lakehouse silver tables for AI research. Transform, validate, and productize multi-modal datasets and pipelines (Databricks/PySpark) to support model development, evaluation, and production ML/LLM workflows while ensuring data quality, provenance, and regulatory controls.

As a Data Analyst, you will analyze user behavior, create interactive dashboards, and collaborate with teams to provide actionable insights for product improvement.