Top Data Engineer Jobs in Toronto

Build and maintain scalable data pipelines, data platforms, reporting infrastructure, reconciliation systems, and self-service analytics tools. Develop customer-facing and internal applications, integrate internal and external data sources, optimize near-real-time processing, ensure data quality and auditability, and evaluate emerging technologies. Collaborate with engineering, product, analytics, security, and data science teams while participating in on-call support.

Designs and demonstrates secure, scalable Azure database and analytics solutions for enterprise customers. Leads technical sales engagements, proofs of concept, hackathons, architecture workshops, migrations, and production deployments. Advises platform leaders, resolves technical blockers, collaborates with engineering, and develops expertise across Microsoft Fabric, Azure Databases, Databricks, Purview, on-premises data warehouses, Hadoop, and competing analytics platforms.

Build and maintain marketing data pipelines connecting Salesforce, Marketo, and cloud data warehouses. Automate campaign reporting, develop SQL datasets and views, improve attribution and data quality, and support executive marketing pipeline reporting. The role also involves Salesforce and Marketo configuration, documentation, and AI-assisted analytics using tools such as Claude. Candidates will collaborate with Marketing Analytics, Demand Generation, and Marketing Operations.

Build and own Snout’s data foundation, including PostgreSQL and dbt warehouses, ingestion pipelines, dimensional models, data quality, documentation, and reliable rebuilds. Reconcile data from veterinary practice-management systems, support LLM/AI tooling, translate stakeholder needs into technical plans, and establish the foundation for a future data team and analyst.

Build and maintain cloud-based ELT/ETL pipelines using dbt, Airflow, and Cloud Composer. Implement data transformations, ingestion workflows, quality tests, monitoring, and troubleshooting. Maintain schemas and lakehouse structures across Snowflake, BigQuery, and Databricks. Document pipelines and collaborate with analytics and data science teams to deliver optimized, reliable data structures.

Build and maintain streaming and batch data pipelines, data lakes, warehouses, and transformation systems using AWS and modern data tools. Collaborate with engineers on data integrity, testing, validation, documentation, and production code. Develop prototypes for uncertain technical solutions, support data visualization infrastructure, lead projects independently, advocate for trustworthy data, and mentor junior engineers.

Build and optimize scalable data pipelines, models, integrations, and workflows supporting analytics, loyalty strategy, and enterprise decision-making. Maintain Snowflake-based data infrastructure, improve automation, CI/CD, observability, data quality, lineage, metadata, and governance. Collaborate with analytics, data science, marketing, product, privacy, security, compliance, and external vendors while helping shape cloud-native architecture and technical standards.

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.

Own and evolve Clutch’s end-to-end data platform, including scalable warehouse architecture, production ELT pipelines, analytics data models, quality monitoring, observability, governance, and DevOps practices. Partner with analysts, data scientists, and business stakeholders to support decision-making and financial services initiatives. Mentor engineers, improve technical standards, and use AI to accelerate development.

Leads the design, deployment, automation, and operation of secure enterprise cloud platforms across AWS and Azure. Builds Terraform infrastructure modules, CI/CD pipelines, and cloud data lake platforms while supporting Kubernetes, GitOps, security controls, governance, monitoring, compliance, reliability, and cost optimization. Collaborates with security teams, participates in audits, and promotes platform engineering best practices across development teams.

Design, build, and scale backend data services, APIs, microservices, distributed systems, and high-volume data pipelines. Optimize PostgreSQL and Oracle databases, improve reliability and performance across regions, diagnose complex production issues, and mentor engineers. Partner with DBA and SRE teams while providing hands-on technical leadership and owning services end to end.

Design, build, deploy, and optimize scalable, high-performance data platform components supporting analytics and machine learning. Responsibilities include distributed streaming infrastructure, API and system design, code and design reviews, performance tuning, production debugging, cross-functional collaboration, and mentoring engineers. The role works with technologies such as Kinesis, Flink, Elasticsearch, Snowflake, and distributed cloud systems.

Design, build, deploy, and optimize scalable AI and machine learning systems using large datasets. Develop predictive models, recommendation systems, personalization engines, forecasting solutions, generative AI applications, and production ML pipelines. Collaborate with data, backend, and product teams to integrate models into applications, while monitoring performance, data quality, drift, reliability, and cost. Establish best practices for evaluation, experimentation, reproducibility, and deployment, and research emerging AI technologies.

Lead electrical design for hyperscale, colocation, and mission-critical data centers. Develop medium- and low-voltage distribution, critical and backup power systems, resilient electrical architectures, load calculations, power-system studies, grounding, and protection solutions. Coordinate multidisciplinary designs, support construction and commissioning, collaborate with clients and stakeholders, mentor engineers, and contribute to proposals and technical presentations.

Lead mechanical design for hyperscale, colocation, and mission-critical data centers. Design HVAC, cooling, heat-rejection, chilled-water, and resilient mechanical systems; perform heat-load calculations, airflow studies, capacity planning, and CFD analysis. Prepare technical documentation, coordinate multidisciplinary teams, support construction and commissioning, collaborate with clients and vendors, mentor engineers, and contribute to proposals and technical presentations.

Design and deliver mechanical systems for hyperscale, colocation, and other mission-critical data centers. Responsibilities include HVAC and cooling-system design, load calculations, equipment sizing, technical documentation, code compliance, multidisciplinary coordination, design reviews, and construction-phase support. The role also involves coordinating with clients, contractors, vendors, and utilities while addressing reliability, redundancy, resiliency, maintainability, energy efficiency, and capacity requirements.

Own NimbleRx’s end-to-end data platform, including ingestion, transformation, storage, querying, access, batch and streaming pipelines, warehouse modeling, security, compliance, and reliability. Optimize PostgreSQL and Java/Spring services, investigate infrastructure issues, create self-service documentation and tooling, apply AI to data workflows, and mentor engineers and analysts. The role partners broadly across product, engineering, analytics, and operations while driving the platform roadmap.

Develop, test, implement, document, and support data and AI products. Translate business requirements into technical solutions, contribute to designs, build reusable frameworks and automation, perform regression testing, resolve production issues through root-cause analysis, and improve delivery and deployment processes. The role requires experience with data engineering, ETL/ELT, cloud platforms, structured and unstructured data, AI concepts, GenAI, prompt engineering, RAG, Agile delivery, and JIRA.

The Lead Data Engineer will architect and develop reporting solutions, integrate AI, analyze complex datasets, and mentor team members to enhance data-driven decision-making.

Build and modernize enterprise reporting solutions using Databricks and Power BI. Responsibilities include developing governed datasets, Delta-table reporting views, semantic models, dashboards, automated report distribution, and data validation processes. The role collaborates with business stakeholders, supports testing and deployments, and optimizes reporting performance. Required expertise includes advanced SQL, data warehousing, ETL/ELT, Databricks, Power BI, DAX, Power Query, and Azure services.

Develop and enhance Grafana data source plugins integrating third-party services and databases. Support plugin reliability and operations in Grafana Cloud, integrate data sources with agentic workflows, improve engineering processes, assist with customer escalations, and collaborate across the Grafana Division. The role requires strong Golang and TypeScript skills, Kubernetes and microservices experience, robust software development practices, and participation in on-call and SRE operations.

Data Engineer Intern supporting large-scale data projects at Lyft. Responsibilities include owning an internship project, improving code quality, writing tests, participating in code reviews, and contributing to continuous integration and deployment. The role requires database experience, SQL and data modeling knowledge, strong computer science fundamentals, and effective communication. This hybrid Toronto internship is available during Summer 2027.

Build and own production-grade data pipelines, source connectors, warehouse transformations, curated data models, reconciliation, and lineage for an AI-native finance platform. Develop dbt projects, orchestrate reliable and rerunnable workflows with Airflow or Dagster, tune warehouse performance, integrate messy enterprise systems, and ensure data correctness, auditability, and tenant isolation. Collaborate with backend and AI teams, establish data contracts, review work, and mentor engineers.

Design, build, and operationalize enterprise data solutions across AWS and Snowflake. Develop Python and DBT ETL/ELT pipelines, integrate diverse data sources, and manage Apache Airflow workflows. Contribute to data warehouses, marts, governance, catalogs, and quality initiatives while applying modern and legacy data platform expertise in an agile environment.

Build and operate high-volume event streaming pipelines and analytical databases. Write production Elixir/Python services, integrate legacy pipelines, enforce event schemas and data quality, and provide monitoring, alerting, and SLA-driven observability for event data products.