Top Data Engineer Jobs in Toronto

Lead design and optimization of data models and pipelines for compliance and risk; standardize metrics and documentation; build data quality, lineage, and monitoring (including AI agents for automation); manage ETL scheduling, on-call pipeline support, and collaborate with product and non-technical partners to translate business needs into automated, production-ready data solutions.

Lead the architecture and delivery of GitLab’s data products, including near-real-time data ingestion, knowledge graph integrations, data marketplaces, observability, and data-sharing APIs. Translate ambiguous 0-to-1 initiatives into roadmaps, resolve cross-team dependencies, ensure operational readiness across deployment surfaces, and mentor engineers. The role requires deep full-stack expertise, technical leadership, and experience with AI-powered orchestration and multi-tenant SaaS systems.

Partner with sales teams to win strategic data security opportunities. Deliver technical presentations, gather customer requirements, lead product evaluations, configure solutions, and design test plans. The role requires extensive pre-sales experience, security and networking expertise, data protection knowledge, AI fluency, and collaboration across matrixed go-to-market and partner teams.

Own and evolve WorkOS’s internal data platform, including ingestion, orchestration, Snowflake, dbt models, data governance, reverse ETL, semantic views, and AI-enabled data workflows. Ensure pipeline reliability, freshness, security, and scalability while partnering with Product, Finance, RevOps, GTM, Engineering, and Security. Build monitoring, runbooks, CI/CD gates, access controls, masking policies, and infrastructure automation across AWS and Kubernetes.

Build and operate Databricks-based data products and pipelines that create, model, and deliver syndicated and custom consumer audience segments. Develop tables, views, jobs, workflows, and real-time processing solutions using Python, SQL, Spark, and Delta Lake. Support marketplace delivery, reporting, observability, metadata, testing, CI/CD, privacy compliance, and AI-assisted development. Partner with data science and commercial teams to translate business requirements into scalable engineering solutions, while promoting architecture and engineering best practices.

Develop and maintain scalable big data infrastructure on Google Cloud Platform, including data ingestion, stream processing, enrichment, monitoring, alerting, and integration platforms. Partner with data scientists and internal teams to support data processing and integration needs. Create technical documentation and participate in a rotating 24x7 on-call schedule.

Build and own production data pipelines, dbt models, semantic layers, and supporting infrastructure across Snowflake, BigQuery, Airflow, Fivetran, Databricks, AWS, and GCP. Partner with stakeholders across business functions to scope requirements, resolve data quality issues, monitor SLAs, support incident response, and apply data governance practices. The role requires autonomous delivery, strong communication, production-grade Python and SQL, and contributions to engineering standards, CI/CD, testing, and documentation.

Design, build, and scale IAM platform services and APIs for authentication, authorization, identity lifecycle, access governance, and compliance. Integrate identity providers and authorization systems, improve scalability and observability, design machine and service authentication, participate in architecture reviews, and mentor engineers.

Build, operate, and optimize AWS-based data ingestion pipelines from Aurora MySQL into Snowflake. Responsibilities include infrastructure engineering, reliability and observability, SLOs, incident response, infrastructure as code, CI/CD, performance and cost tuning, source onboarding, and security controls for payment-compliant environments. The role also involves applying AI tools to improve development, testing, runbooks, and documentation.

Designs enterprise-scale data processing frameworks and Azure-based data solutions, develops AI-powered automation, leads complex technical initiatives, mentors engineers, and influences data engineering strategy. The role requires advanced Python, Azure data services, data warehousing, distributed computing, and architecture expertise, along with stakeholder collaboration and technical leadership.

Design, build, and optimize large-scale batch and near-real-time data pipelines processing billions of records. Develop Spark-based ETL/ELT workflows, Airflow orchestration, AWS data infrastructure, data quality monitoring, and production observability. Use AI-assisted development tools to improve engineering productivity while validating security, quality, and performance. Optimize distributed workloads, automate deployments, support compliance analytics, and partner with technical and business stakeholders to deliver reliable, scalable data solutions.

Designs and trains computer vision models for vehicle damage detection, builds high-throughput containerized model-serving microservices, and manages the end-to-end ML lifecycle. The role includes developing Kafka-based streaming data pipelines, optimizing models for real-time inference, maintaining annotation and evaluation frameworks, and collaborating with stakeholders to deliver scalable production solutions.

Develops end-to-end computer vision and machine learning solutions for vehicle damage detection. Responsibilities include training robust models, building high-throughput containerized REST/gRPC model-serving microservices, managing the full ML lifecycle, optimizing real-time inference, collaborating with stakeholders, and maintaining Kafka-based streaming data pipelines. The role also involves annotation and evaluation frameworks, cloud backend engineering, and technical design leadership.

Senior software developer responsible for building and operating scalable backend services for a VIN Decoder platform. The role develops Python APIs, services, and workers using PostgreSQL, Redis, AWS, and GCP BigQuery; designs data models and optimized SQL; integrates third-party APIs; and drives reliability, observability, testing, incident response, and operational maturity. The developer also contributes to architecture, code reviews, documentation, Agile delivery, on-call support, and technical mentorship.

Design, build, and support production data feeds and integration pipelines using Python, SQL Server, Snowflake, REST APIs, SFTP, Docker, and AWS ECS. Develop incremental-loading workflows, apply Data Vault modeling, troubleshoot end-to-end data pipelines, and implement validation, monitoring, logging, error handling, and retries. Collaborate with technical and business stakeholders while maintaining pipeline and operational documentation.

Develops, deploys, and monitors production data and machine learning pipelines. Responsibilities include data preparation, exploratory analysis, predictive modeling, feature and algorithm selection, model deployment, infrastructure development, and monitoring model performance, data quality, drift, latency, and reliability. The role also requires communicating insights, collaborating cross-functionally, and translating business problems into scalable data and ML solutions.

Owns the design, administration, security, monitoring, automation, reliability, and modernization of enterprise data platforms across SQL Server, Azure, and Microsoft Fabric. Responsibilities include high availability and disaster recovery, performance tuning, access governance, data integration, CI/CD deployments, incident response, platform standards, and cloud migration. The role collaborates with data, infrastructure, security, architecture, and application teams to maintain secure, scalable, recoverable, and cost-effective data services.

Designs, builds, and operates scalable batch and near-real-time data pipelines across Microsoft Fabric and Azure. Develops ETL/ELT solutions, data models, monitoring, governance, security, CI/CD, and data quality controls. Leads technical design, modernization of legacy workloads, production support, performance optimization, documentation, and engineering standards while partnering with analysts, architects, developers, and business stakeholders.

Designs and optimizes Azure SQL databases, data warehouses, ETL pipelines, data models, and Lakehouse solutions for datasets exceeding 5TB. Develops complex SQL queries, stored procedures, functions, and triggers; performs performance tuning and troubleshooting; and supports Azure infrastructure. The role also contributes to technology roadmaps, leads complex backend projects, conducts code reviews, and mentors developers while using tools including PySpark, Azure Data Factory, Databricks, and Delta tables.

Implements and configures an AI platform by integrating customer data, building ETL workflows, troubleshooting pipelines, managing recurring data deliveries, and automating manual processes. The role collaborates with customers and internal teams to define technical requirements, resolve implementation issues, deploy tailored solutions, and maintain data security and privacy.

Own complex customer data implementations from technical discovery through production go-live. Build automated ETL workflows, scalable data ingestion solutions, validation frameworks, and data-quality processes using Python, SQL, PySpark, Snowflake, and AWS. Troubleshoot integrations and production issues, ensure compliance with security and privacy requirements, mentor junior engineers, and improve implementation standards and tooling while working directly with technical and non-technical customer stakeholders.

Build and scale data platforms, ETL pipelines, data lakes, warehouses, feature stores, and orchestration tooling. Integrate clickstream, relational, and unstructured data using Databricks and AWS. Partner with data scientists, ML engineers, product managers, and business stakeholders to support analytics, predictive modeling, and data-driven decisions. Maintain production systems, automate testing and monitoring, and communicate technical roadmaps and challenges.

Develops and supports ETL applications and data pipelines across legacy and modern platforms. Responsibilities include systems analysis, design, construction, testing, implementation, operational support, troubleshooting ETL processes and SQL workloads, performance tuning, and continuous improvement. The role requires expertise in ETL architecture, data warehousing, Unix scripting, Oracle or MySQL, application integration, and database development, with exposure to streaming technologies and modern languages such as Spark, Scala, Java, or Python.

Provides technical leadership for a scalable data engineering platform on Databricks. Designs reusable frameworks, production pipelines, engineering standards, monitoring, and operational practices using Python, SQL, and Spark. Partners with Architecture, Data Science, ML Engineering, Product, and technology teams to integrate data capabilities, translate commercial real estate requirements, mentor engineers, conduct code reviews, and influence cross-team technical direction.

Build end-to-end internal products and tooling for robotics dataset delivery, including review and QA interfaces, dashboards, data viewers, export and validation logic, pipelines, APIs, and data infrastructure. Convert manual workflows into reliable tools, take ambiguous problems from concept to shipped v1, and iterate with users. The role spans front-end, backend, data systems, and product development, supporting petabyte-scale video and sensor datasets used by AI labs and robotics companies.