Top Data Engineer Jobs in Toronto

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.

Designs and leads scalable AWS and Databricks data pipelines, data models, quality frameworks, monitoring, testing, and production deployments. Investigates ambiguous data and business problems independently, translating requirements into secure, reliable technical solutions. Mentors engineers, establishes architecture and coding standards, and partners with architecture, analytics, product, and business teams. Supports AI readiness, data unification, metadata strategy, and enterprise integration initiatives.

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.

Lead a team to deliver analytics solutions, transforming data into actionable insights, building AI/ML models, and collaborating across business units to drive analytics adoption.

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.

Design and build durable, distributed data-streaming infrastructure that decouples process data from Camunda’s live execution engine. Develop customer-facing integrations such as Kafka connectivity, support analytics data consumption at scale, and make architecture decisions around consistency, durability, and storage. Participate in production support and on-call rotations while collaborating with Product, Data Layer, Distributed Systems, QA, SRE, and Support teams. Lead ambiguous, cross-functional engineering initiatives from design through delivery.

Build and maintain Camunda 8 backend data-layer services spanning relational databases and Elasticsearch/OpenSearch. Design scalable storage, query, retention, and streaming systems; improve performance, indexing, and production tooling. Lead engineering initiatives from solution design through delivery, collaborate across teams, provide L3 support, and participate in on-call rotations. Investigate production data issues and contribute to schema evolution, workflow automation, and CI improvements.

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.

Design, develop, and maintain scalable Databricks-based ETL/ELT data pipelines and transformations. Support migration from HANA to a modern lakehouse, ensure data quality and performance, troubleshoot issues, participate in code reviews, and collaborate with architects, analysts, and stakeholders to deliver enterprise reporting, analytics, and AI-ready data solutions.

Lead the design and implementation of scalable Java microservices, data platforms, and batch and real-time pipelines. Optimize lakehouse technologies, event-driven architectures, cloud-native deployments, data quality, governance, observability, and performance. Establish schema contracts, testing, monitoring, SLAs, SLOs, and automated validation. Provide technical leadership, mentor engineers, collaborate with product, analytics, and AI teams, and drive modernization and engineering best practices as a hands-on contributor.

Build and maintain production-grade data processing systems and foundational infrastructure for enterprise AI. Collaborate with researchers and engineers, deliver end-to-end implementations, transform unstructured data into performant datasets, and use distributed processing frameworks and modern analytics tooling to inform products and strategy.

The AI-Native Data Engineer will build data pipelines, architect databases, design GCP infrastructure, and automate workflows for energy management using AI.

The Data Engineer will provide technical support and deployment expertise, review data designs, and ensure code quality while collaborating with international teams on a strategic data initiative.

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

Design and implement secure, resumable cloud pipelines to ingest multi-terabyte medical imaging data; build GPU-accelerated orchestration with strong retry semantics and cost controls; define data lifecycle and retention; implement HIPAA/PHI security and compliance controls; and deliver operational tooling (monitoring, alerting, runbooks) for a device fleet.

Leads and mentors data engineering teams while designing, deploying, and operating scalable data pipelines and platforms supporting analytics, AI/ML, and commercial use cases. Provides architectural leadership across cloud-native platforms, distributed processing, orchestration, data integration, and streaming architectures. Manages delivery priorities, stakeholder requirements, operational excellence, incident response, standards, and continuous improvement across enterprise data engineering initiatives.

Conduct research and design AI data-security and privacy architectures for endpoint devices. Prototype and build privacy-preserving defenses, analyze emerging attack vectors and regulations, and collaborate with global labs and product teams to transition research into production-grade endpoint security solutions.

The Data Engineer will build and manage scalable data pipelines and databases, collaborate on architecture, and drive data systems towards real-time capabilities in a fast-paced, innovative environment.

Lead and mentor a team of developers, oversee cloud product delivery, drive engineering practices, and ensure high-quality standards while contributing to cross-functional initiatives.

Design, develop, and maintain scalable data pipelines, models, ingestion, transformation, and integration solutions using Databricks and Microsoft Azure. Troubleshoot performance and data quality issues, optimize workflows, support migrations and modernization, and collaborate with architecture and engineering teams. Translate business requirements into technical solutions while following security, governance, quality, and engineering best practices.

Build highly available, scalable distributed systems and real-time data services supporting Internet-wide scanning, DNS resolution, certificate ingestion, security research, and customer applications. Responsibilities include developing data pipelines, messaging systems, databases, caching layers, tooling, frameworks, and cloud or on-premises services; productionizing prototypes; and collaborating with engineering, product, machine learning, and security teams. The role emphasizes maintainable, testable code, reliability, monitoring, and long-term platform development.

Leads the design and delivery of scalable data pipelines, dbt models, orchestration workflows, and analytical data models. Establishes data architecture, engineering standards, testing, observability, governance, and quality practices. Partners with stakeholders to deliver data-driven solutions, supports incident response, and provides technical leadership through design reviews, mentorship, hiring, and team development.