Top Data Engineer Jobs in Toronto

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.

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.

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.

Build and improve scalable full-stack products for higher education, owning initiatives across the software development lifecycle from discovery through release. Collaborate with Product, Design, and Engineering in a cross-functional Scrum team, experiment with AI technologies, resolve bugs, complete tickets, and deliver reliable, tested, user-focused features.

Design, develop, and own scalable real-time and batch data pipelines using Kafka, Spark, Flink, Airflow, Databricks, and AWS. Implement data quality frameworks, oversee projects from design through long-term support, identify scalability risks, document systems, and collaborate with data science, product, and engineering teams to guide technical roadmaps.

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.

Design, build, and maintain Spark-based data pipelines and platform services for sales compensation calculations. Improve data modeling, reliability, observability, testing, and scalability. Partner with Product, Engineering, and Analytics to translate business requirements into deterministic, production-grade data systems and participate in design and code reviews.

Design, build, and maintain scalable data platforms, architectures, and pipelines using Azure Fabric, Databricks, and Snowflake. Optimize data workflows for reliability, scalability, performance, security, and compliance. Perform data modeling and ETL/ELT, improve data quality, and collaborate with analysts, scientists, engineers, and stakeholders to deliver tailored data solutions.

Design, build, and maintain scalable real-time and batch data pipelines and data lake architecture using AWS and big-data tools. Own end-to-end AI/ML workflows, perform feature engineering and statistical modeling for operational efficiency and financial forecasting, and provision infrastructure with Terraform/CloudFormation while optimizing pipeline performance.

Design, deploy, and maintain AWS and Databricks data infrastructure, data lakes, and scalable ETL pipelines. Build custom ingestion services and connectors using Airbyte, Go, and Python; optimize performance, reliability, monitoring, and data quality. Implement cloud data governance, security, access controls, encryption, and compliance practices while collaborating with platform engineers, analysts, and stakeholders.

The Senior Data Engineer will scale the data ecosystem, construct data models, build systems for KPI tracking, and ensure data safety.

The Data Engineer will assist clients with SAP security design, implement SAP security, develop authorization profiles, and conduct security reviews.

Build scalable data pipelines, ELT processes, infrastructure, and dimensional data models supporting internal analytics and customer-facing data products. Implement data quality monitoring, auditing, alerting, warehouse optimization, and governed semantic layers for AI and LLM-enabled analytics. Partner with technical and business stakeholders, improve data processes using AI, and deliver reliable metrics from diverse data sources.

Build highly available, scalable distributed systems and real-time services supporting Internet-wide scanning, data ingestion, APIs, security research, and machine learning initiatives. Responsibilities include developing data pipelines, messaging systems, databases, caching layers, tooling, and cloud or on-premises services; productionizing prototypes; collaborating with engineering, product, machine learning, and security teams; and maintaining reliable, observable systems.

Build and maintain scalable financial data infrastructure, including ingestion, validation, normalization, and integrations. Develop systems that calculate portfolio performance metrics efficiently across high-volume datasets, improve reliability and observability, and support distributed processing. Create internal tools, dashboards, reusable schemas, asset data feeds, and self-service analytics capabilities. Collaborate cross-functionally to expand market-data coverage, support B2B integrations, and strengthen operational resilience.

Architect and build core distributed systems for high-throughput real-time and batch data ingestion, processing, and storage. Ensure fault-tolerance, scalability, and strong query performance for time-series and spatial data. Develop automated tooling for data quality and lineage, collaborate on reusable abstractions, and write production-ready code (Java/Python).

Lead design and implementation of a scalable data platform handling petabytes of telematics and time-series data. Build and optimize streaming and batch pipelines, design storage solutions, implement observability and data lineage, manage HA infrastructure, and write maintainable Java/Python code while shaping long-term architectural strategy.

The role involves building and optimizing big data pipelines, designing data architectures, collaborating with data scientists, and driving technical strategy for data processing infrastructure.

Triage and resolve Master Data Management (Party, Risk, Reference) issues in Stibo STEP, run targeted data-cleansing sprints, monitor data quality dashboards, support releases/UAT, and maintain documentation and playbooks to prevent recurring data issues.

Design, build, and maintain scalable data pipelines and tenant-aware APIs to ingest and transform data from ad platforms, order systems, and real-time events. Ensure reliability, observability, and maintainability across event-driven and batch architectures, implement authentication flows, optimize data workflows in a cloud-native environment, and collaborate with product, infrastructure, and data teams to onboard new sources.

Design and optimize SQL Server solutions, manage data, automate processes with PowerShell, and integrate with external systems to enhance functionality.

Lead design and implementation of Jane's customer data infrastructure: own event collection, pipelines, CDP tooling, schema governance, identity resolution, and attribution. Work cross-functionally with marketing, BI, product, and engineering to set standards, evaluate tooling (e.g., RudderStack), ensure privacy-compliant instrumentation, and build reliable, consistent data delivery to the warehouse.

Build and operate scalable data ingestion, transformation, and connector frameworks; design and maintain a knowledge-graph-based data fabric; normalize and vectorize enterprise data for LLM/AI workflows; implement governance, lineage, access controls, and secure APIs to serve ML/agent pipelines.