Top Data Engineer Jobs in Toronto

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.

Lead ERP data conversion activities: extract, cleanse, transform, validate and load master, reference, and transactional data. Build and optimize ETL pipelines, mappings, validation routines and load files. Support mock conversions, cutover rehearsals, reconciliation, error remediation, data quality metrics, and documentation while working with SMEs and ERP teams.

Lead a team to define and implement enterprise data quality strategies, design and run automated data quality and regression tests, validate end-to-end ETL/ELT pipelines, perform source-to-target reconciliation and data profiling, collaborate with stakeholders, and establish data quality metrics and governance to ensure reliable reporting and analytics.

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.

The role involves designing scalable data pipelines for 3D, video, and sensor data, optimizing infrastructure, and productionizing ML models with researchers.

Build and operate a reliable data platform: monitor infrastructure and pipelines, automate platform tasks, manage IaC and CI/CD, support RBAC and governance, contribute to data pipelines and migration, document runbooks, and participate in on-call incident response while improving AI-assisted engineering workflows.

Lead technical pre-sales engagements for commercial customers to design and deploy secure, scalable cloud database and analytics solutions. Build PoCs, run hackathons and architecture workshops, resolve technical blockers, and partner with engineering to accelerate migrations to Microsoft Fabric, Azure Databases and analytics services. Represent Microsoft in community forums and help customers modernize data platforms, governance, and AI-enabled analytics workflows.

Own cloud operational health, architecture intelligence, and cost optimization. Analyze cloud spend, build dashboards/reporting, enforce governance, review architecture for cost impact, and translate technical findings for engineering, finance, and executives.

Design and build BI dashboards and analytics products (Tableau); translate business requirements into technical designs; collaborate with data architects and engineering teams; ensure data quality and perform root-cause analyses; enable self-service analytics and conduct code reviews and performance tuning.

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.

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.

As a Backend Engineer on the Data team, you will design and maintain data pipelines and services, ensuring data quality and scalability while collaborating with cross-functional teams to meet data needs.

The role involves designing and building data pipelines for analyzing user feedback on AI models, ensuring data quality and infrastructure scalability.

Design and maintain data warehouse systems, develop ETL pipelines, manage datasets for analytics, and implement real-time data solutions.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

Responsible for designing, building, and maintaining scalable data pipelines using Databricks. Optimize ETL/ELT processes and ensure data quality, governance, and security.

Build and maintain scalable, reliable data systems and pipelines, uphold SLOs, partner with Product/Sales/Finance, enable self-service analytics, contribute to data platform strategy, and improve engineering practices for testing, reliability, and documentation.

Own and drive the architecture and roadmap of ClickUp's data platform. Build scalable, reliable data pipelines and AI/ML infrastructure using AWS serverless, Snowflake, dbt, and Terraform. Lead cross-team technical initiatives, optimize cloud costs, establish engineering standards (observability, testing, CI/CD), mentor senior engineers, and influence org-wide architecture decisions.

Lead technical strategy and hands-on implementation for Faire's data platform. Architect and scale ETL/ELT, streaming and batch pipelines, data warehousing, orchestration, monitoring, cost optimization, security, and governance. Mentor teams, prioritize platform roadmaps, and partner with product, analytics, and data science to enable self-serve data capabilities across the company.

Build and scale AI data and ML infrastructure: evaluation systems, fine-tuning pipelines, agent-first product surfaces, high-throughput data workflows, and integrations of new models into production.

As a Staff Software Engineer, you'll architect and build foundational platform services, focusing on data infrastructure, healthcare interoperability, and secure APIs while mentoring other engineers.