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, build, and maintain end-to-end data pipelines and data models in Snowflake using Medallion Architecture. Develop ingestion and transformation logic with Python and/or Informatica, implement CI/CD and version control (Git/Bitbucket), leverage Snowflake AI/ML (Cortex/LLM integrations), monitor and optimize pipelines, and ensure data quality and performance across environments.

The Senior Data Engineer will develop and maintain data platforms on Google Cloud, optimize data pipelines, and support Data Science teams in model training and analytics, collaborating closely across departments.

Design, implement and maintain enterprise BI and cloud data platform solutions. Develop BI reports, dashboards and workflows; automate processes; monitor production and UAT; troubleshoot complex issues; evaluate emerging BI and cloud technologies; collaborate with stakeholders to deliver scalable, customer-centric BI services.

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, develop, test and maintain cloud-based data applications and infrastructure on AWS. Build and optimize ETL and streaming pipelines, support CI/CD and infrastructure as code, and collaborate with stakeholders to improve performance and deliver technical solutions independently.

Design, develop, and maintain scalable ETL/ELT pipelines and data architecture on Azure (ADF, Databricks, Synapse). Ensure data quality, governance (Unity Catalog), automation, CI/CD, and collaborate with data scientists, analysts, and business teams to enable analytics and operational use.

Design, develop and maintain Azure-based ETL pipelines and data ingestion/curation solutions. Lead POCs, ensure data quality, compliance, automation, and monitoring. Coordinate stakeholders, follow Agile practices (JIRA), provide L3 production support, and uphold development and deployment standards for batch and real-time analytics.

Design, develop, and maintain ETL/data pipelines on Azure to support analytics. Lead prototyping, data ingestion/curation, automation, monitoring, and L3 production support while ensuring data quality, compliance, and stakeholder coordination.

Design, build, and optimize scalable data pipelines and lakehouse solutions. Work with large datasets using Python, SQL, Spark, DBT, and Airflow. Integrate platforms via APIs, tune Databricks/Parquet/Delta performance, support CI/CD deployments, and maintain high availability in a hybrid agile environment.

Join the Cloud Data Ingestion & Transformation team to design, build, and operate scalable data ingestion and transformation systems. Own end-to-end features (primarily back-end with front-end delivery), process very high data volumes, drive architectural decisions, and improve platform reliability and customer data integrations.

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 Data Engineer collaborates with stakeholders to develop data solutions, optimize pipelines, manage data quality, and support analytical needs, focusing on capital markets data.

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 AI engineering on the Data Platform: build Databricks Apps deployment paths, construct an AI layer over the Medallion Architecture and semantic layer, enable LLM/NLP systems and agentic workflows, embed AI tooling in the developer lifecycle, enforce guardrails for production safety, define metadata/modeling patterns, and mentor engineers to accelerate AI adoption.

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

Design, build, and maintain Snowflake-based data pipelines using dbt and Airflow. Own data modeling (dimensional modeling, SCDs), ingestion (batch and event-driven), data governance, observability, and tooling so teams can access reliable, documented analytics-ready datasets. Collaborate with analysts to define metrics and resolve quality issues.

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

Design, develop, and maintain scalable data pipelines on Azure Databricks. Translate business requirements into code, perform impact analysis, participate in code reviews, support production and UAT, and troubleshoot complex application issues to improve client-facing data solutions.

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 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.

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.

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.