Top Data Engineer Jobs in Toronto

The Data Engineer will develop and maintain data pipelines, ensure data quality, and collaborate with business teams to implement insightful data solutions.

As a Principal Data Engineer, you will design and implement LLM, AI-powered security data platforms, mentor engineers, and drive the adoption of data solutions across teams.

Design and maintain Azure DevOps CI/CD pipelines and automate deployment of Microsoft Fabric, Azure Data Factory, Synapse, Databricks, and Power BI. Implement IaC (Bicep/ARM), manage Azure environments, security, secrets, monitoring and logging, and collaborate with cross-functional teams to improve automation and platform delivery.

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.

Lead design and build scalable data architectures, ETL pipelines, ingestion and data quality frameworks. Collaborate with cross-functional teams, enforce data governance, analyze data for insights, mentor junior engineers, and serve as the technical authority on data engineering best practices.

Design, build, and maintain scalable data pipelines and storage (data lakes/warehouses). Integrate diverse sources, enforce data quality, security and compliance, optimize ETL/ELT, monitor pipelines, and mentor junior engineers while collaborating with data scientists, analysts, and stakeholders.

Design, develop, and maintain data models and complex ETL pipelines; support data acquisition/ingestion, lineage, governance, and metadata. Use Azure, Databricks, PySpark, Python, SQL, and ADF to resolve data issues, enable testing and deployments, advise stakeholders, and mentor team members.

Design, build, and maintain end-to-end data pipelines in Snowflake using Medallion Architecture. Develop ETL/ingestion with Python and/or Informatica, implement Snowflake AI/ML features, manage version-controlled code (Git/Bitbucket), support CI/CD, monitor and optimize pipelines, and ensure data quality and performance across environments.

Design, build, and maintain scalable data infrastructure and ETL pipelines using Python, SQL, PySpark, and Microsoft Fabric. Collaborate with Analytics and Data Science to translate requirements, optimize queries, investigate and resolve data issues, and support reliable pipeline operations and system architecture.

Design, build, and maintain reliable, scalable ETL/ELT pipelines, support data migrations, prepare analytics and AI-ready datasets, implement data quality and observability, support Power BI models, and collaborate with analytics, IT, governance, and AI teams to deliver production data solutions.

Lead design and implementation of a federated Lakehouse data platform, unify data silos, define governance/observability, enable secure AI/ML (RAG/vector readiness), mentor architects/engineers, and present roadmaps to executives.

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

The Data Engineer will architect and implement advanced analytics capabilities, manage data pipelines, and optimize data processing using AWS cloud and various analytical tools.

The Principal Data Engineer at RAVL will design and implement enterprise-grade data and AI platforms, mentor engineers, and influence architectural standards across teams.

The Data Engineer will design and manage data pipelines and architectures, ensuring data quality and security while collaborating with cross-functional teams for effective analytics and reporting.

Own Venn's data infrastructure, build reliable data pipelines, design trusted data models, and improve data quality across the organization to support business decisions.

The Data Engineer will analyze and interpret data to drive insights, create dashboards, and enhance product performance, collaborating with cross-functional teams.

Design and build scalable data systems and pipelines, migrate legacy systems, ensure data governance, and collaborate with cross-functional teams to enhance data accessibility and reliability.

Design, build, and maintain Databricks/Spark ETL pipelines and Bronze-Silver-Gold architectures; implement event-driven streaming with Pub/Sub and Protocol Buffers; prepare datasets for LLM fine-tuning and embeddings; integrate third-party sources (Shopify, Klaviyo); implement attribution, CLV, and analytics models; ensure data quality, monitoring, and idempotent retry-safe pipelines.

Design, build, and deploy scalable, secure data solutions on-premise and in Azure. Collaborate with engineering, security, and ops to implement modern data practices (Databricks, Snowflake, Python, SQL), produce solution designs, support project delivery, and promote DevSecOps and cloud best practices.

Build, deploy, and maintain production data pipelines using Airflow/Cloud Composer and BigQuery. Design efficient data models, optimize queries and pipeline performance, and deliver custom client solutions from prototype to production. Collaborate with analysts, Product, and Customer Success; continuously improve data infrastructure and leverage AI tools to accelerate development and debugging.

Design, develop, and maintain ETL/ELT pipelines and automated data extracts from the Gold Layer. Build formatted reports and secure outbound feeds, implement data validation and lineage, optimize performance, and manage scheduling/monitoring. Collaborate with Finance, Actuarial, and operations to translate reporting needs and document technical specifications.

Lead design, development, and maintenance of scalable cloud data solutions using Azure Data Factory, Azure Databricks, Python/PySpark and SAS. Build and optimize data pipelines, apply data modeling, create unit tests, support data mesh adoption, and mentor team members while driving technical strategy for enterprise-scale projects.

Design, develop, and maintain scalable real-time and batch data pipelines and data lake architecture. Build end-to-end AI/ML workflows including feature engineering, model training, and real-time inference. Collaborate with Finance, Operations, and Product to deliver analytics and financial forecasting solutions while provisioning infrastructure with Terraform/CloudFormation and optimizing pipeline performance.