Top Data Engineer Jobs in Toronto

Design, build, and manage Northland's Azure-based enterprise data platform. Lead data architecture, develop scalable ETL/ELT pipelines, model enterprise data, ensure data quality and governance, deliver self-service data marts and Power BI datasets, and provide technical leadership to support analytics and AI initiatives.

Design, develop, test and support finance data solutions using Azure, Databricks, Python and Spark. Lead technical design, implement metadata-driven data pipelines and Bronze/Silver/Gold data lake patterns, support CI/CD DevOps processes, coordinate with finance stakeholders and vendors throughout implementation and delivery.

Design and maintain data architecture and pipelines to support compliance and risk teams. Build and optimize data models, standardize metrics, and create data dictionaries. Implement data quality, lineage monitoring, AI-driven agents for false-positive reduction and automation, and participate in on-call rotations to ensure SLAs are met.

The Senior Pyspark Data Engineer will manage large-scale data processing systems, conduct feasibility studies, provide user support, and develop security measures while acting as a subject matter expert in applications development.

This role involves designing and developing scalable data solutions, automating tasks, and collaborating with teams to deliver business value. Responsibilities include strong database development using SQL and Python, as well as leading innovative projects and ensuring compliance.

As a Data Engineer, you will design and implement solutions for data integration and manage data quality, while collaborating with various teams for optimal data usage and decision-making.

Design, build and maintain Azure-based data ingestion, ETL and curation pipelines (batch and real-time). Collaborate with stakeholders, prototype solutions, ensure data quality and compliance, automate/monitor analytics services, provide L3 production support, and follow agile practices with code reviews and CI/CD.

Design, build, and scale real-time and batch data infrastructure and pipelines (Kafka, Databricks, Snowflake) to power analytics, reporting, and AI-powered data experiences. Drive data modeling, governance, observability, and best practices while partnering with cross-functional teams and mentoring engineers.

Design, build, and support data services, pipelines and internal data architecture. Collaborate on product development, analyze and organize raw data, improve data quality, define best practices, and identify acquisition opportunities to support business insights and member-facing services.

Design, build, and optimize Microsoft Fabric-based ingestion, transformation, and curated data layers. Migrate legacy sources, implement business rules, develop API-driven workflows, ensure data quality, security, and monitoring, and support CI/CD and operational delivery for analytics and reporting.

As a Data Engineer, you'll design, build, and maintain data pipelines, ensure data quality, and collaborate with stakeholders to deliver data solutions.

Build and maintain integrations and data pipelines connecting client databases, APIs, and SaaS tools. Extract, clean, transform, and move messy real-world data to feed retrieval and agentic AI systems, apply security and privacy best practices, and document reproducible integrations.

As a Data Engineer, you will design and implement scalable data pipelines and solutions, collaborate with teams, and apply new technologies in an Agile environment.

The Data Engineer will design scalable data pipelines, manage data solutions, collaborate with teams, and ensure project delivery on time while applying new technologies.

Design, develop, and optimize big data solutions using Databricks, Apache Spark, and cloud platforms. Collaborate with teams to ensure high-performance data architectures.

As an Associate Data Developer, you will support data systems, work on cloud migration, create analytics systems, and analyze data for improved performance.

The Data Engineer will design and maintain data pipelines on cloud data warehouses, manage integrations with advertising platforms, ensure data quality, and collaborate with cross-functional teams to meet reporting needs.

Design, build, and maintain production data pipelines using Airflow/Cloud Composer and BigQuery; define data models and optimize queries; deliver custom solutions from POC to production; collaborate with Data, Product, and Customer Success; deploy dashboards/data apps; continuously improve data infrastructure and leverage AI tools to accelerate development.

Join a talent network connecting data engineers with VC-backed startups. Build scalable batch and real-time pipelines, data models, warehouses/lakehouses, ensure data quality, support ML pipelines, integrate diverse data sources, and define data architecture and governance.

Build and maintain scalable data pipelines, dbt models, and warehouse infrastructure to deliver reliable data for analytics and BI. Collaborate with cross-functional teams to enforce data quality, documentation, and governance; monitor pipeline health, investigate and resolve issues, and support data migration and automation efforts.

Design, build, and maintain core data pipelines and models for Lyft's Safety and Customer Care platform. Scale processing, tune Spark/SQL/MapReduce jobs, implement data quality systems, enable self-service ETL tooling, and collaborate with product, engineering, and analytics teams to deliver reliable reporting and insights.

As a Data Engineer, you'll develop and manage data ingestion pipelines, model data for analytics, and ensure data quality across advertising platforms.

Design and implement scalable data ingestion and onboarding pipelines, build automation and Claude-powered tooling to reduce production MTTR, integrate third‑party sources (Veeva/Salesforce), design APIs and warehouse schemas, and collaborate cross-functionally to deliver production-grade data solutions on AWS and Snowflake.

Build and maintain real-time and batch data ingestion and transformation pipelines using AWS and modern data tools. Design data models, consolidate sources into data lakes/warehouses, collaborate with engineers on data integrity, produce prototypes for unknown solutions, and support deployment of data models and dashboards for analytics and visualization.

Design, build, and maintain scalable data pipelines and infrastructure using Spark, Kafka, cloud platforms and containerization. Ensure data integrity, ETL, high availability, performance tuning, observability, and collaborate with AI/data science teams to enable analytics and ML solutions.