Top Data Engineer Jobs in Toronto

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.

The Data Engineer will develop and maintain data pipelines, ensure data quality, and collaborate with business teams to implement insightful data solutions.

Design and maintain Azure DevOps CI/CD pipelines and automate deployment of Microsoft Fabric, Azure Data Factory, Synapse, Databricks, and Power BI. Implement IaC (Bicep/ARM), manage Azure environments, security, secrets, monitoring and logging, and collaborate with cross-functional teams to improve automation and platform delivery.

Design, build, and operate production AI systems and marketing data infrastructure. Develop retrieval pipelines, agent orchestration, CDP integrations, ETL/ELT pipelines, and analytics. Automate workflows with AI, ensure data quality and reliability, partner with stakeholders, ship Python and SQL code, mentor engineers, and drive requirements to production.

As a Principal Data Engineer, you will design and implement LLM, AI-powered security data platforms, mentor engineers, and drive the adoption of data solutions across teams.

Design, build, and maintain end-to-end data pipelines in Snowflake using Medallion Architecture. Develop ETL/ingestion with Python and/or Informatica, implement Snowflake AI/ML features, manage version-controlled code (Git/Bitbucket), support CI/CD, monitor and optimize pipelines, and ensure data quality and performance across environments.

Design and build scalable data systems and pipelines, migrate legacy systems, ensure data governance, and collaborate with cross-functional teams to enhance data accessibility and reliability.

Design, build, and operate a unified data platform: ingesting, processing, and exposing large volumes of data. Develop scalable ingestion pipelines, storage layers, observability, SLIs/SLOs, multi-tenant models, and metadata/ governance to enable secure cross-product data access and migrations from legacy systems.

Design, build, and optimize ETL pipelines, data schemas, and data tools to support analytics and data science. Tune SQL and databases for performance and collaborate with teams to scale data infrastructure using Snowflake, Python, and related tools.

Lead design, development, and optimization of Databricks/Spark-based ETL/ELT pipelines on Azure. Drive architecture decisions for lakehouse and data warehouse layers, improve performance, enforce data quality and governance, mentor junior engineers, and collaborate across stakeholders to deliver production-ready data solutions.

Ingest, process and transform large-scale behavioral data from web, mobile and connected TV into advertising data solutions. Build and maintain Big Data pipelines (BigQuery, Dataflow, Spark, Airflow), manage hybrid cloud infrastructure, generate audience segments and integrate with DMP/DSP systems, create visualizations, define data delivery architecture, and collaborate with data scientists, developers, and product teams to drive best practices and production-ready solutions.

Design, build, and operate production ML systems that generate trusted signals for ranking, retrieval, recommendations, propensity/churn/LTV, and next-best-action decisioning. Define signal/data contracts, own feature and candidate generation through serving, experimentation, monitoring, and feedback loops, and evaluate long-term business impact, trust, fairness, and compliance. Partner across product, data, modeling, risk, and compliance and apply AI/agents to accelerate engineering and operations.

Design, build, test, and deploy production-grade data pipelines, APIs, and distributed PySpark workloads on Microsoft Fabric and Azure. Use Python, dbt, Airflow, and Docker to package and orchestrate workloads, implement data quality, observability, lineage, and governance (Purview), and collaborate with governance, analytics, and IT teams to deliver scalable, secure, production-ready data platform solutions.

Lead design and implementation of scalable data models, ETL/ELT pipelines and migrations to Snowflake/Databricks. Optimize data workflows, ensure data quality and governance, support analytics reporting, and provide technical leadership to junior engineers while collaborating with stakeholders to deliver data-driven solutions.

Seeking an experienced Data Engineer to design and maintain data pipelines and infrastructure, collaborating with data scientists and stakeholders.

Design, develop, and maintain scalable data pipelines on Azure Databricks. Translate business requirements into code, perform impact analysis, participate in code reviews, support production and UAT, and troubleshoot complex application issues to improve client-facing data solutions.

Design, build and optimize BI assets (QuickSight dashboards, semantic data models) on SQL datasets. Develop and maintain data pipelines, perform SQL tuning, ensure data quality and governance, and support the web presentation layer. Collaborate with business partners to deliver accurate, performant, secure reporting and analytics.

Design, build, and productionize ML models and infrastructure for ranking, recommendations, personalization, and pricing. Develop feature engineering systems, ETL/ELT and inference pipelines, deploy and monitor models, ensure observability, performance SLOs, cost optimization, and MLOps automation.

Design, build, and maintain scalable data pipelines and storage (data lakes/warehouses). Integrate diverse sources, enforce data quality, security and compliance, optimize ETL/ELT, monitor pipelines, and mentor junior engineers while collaborating with data scientists, analysts, and stakeholders.

Lead the design, build, and support of scalable on-premise and Azure cloud data platforms, ETL/ELT, streaming and batch solutions, data lakes/warehouses, APIs and AI-enabled applications. Drive migrations, deployments, automation, performance tuning, and compliance for large-scale fintech data environments while partnering with product, analytics, architecture and engineering teams.

Design, build, and maintain Databricks/Spark ETL pipelines and Bronze-Silver-Gold architectures; implement event-driven streaming with Pub/Sub and Protocol Buffers; prepare datasets for LLM fine-tuning and embeddings; integrate third-party sources (Shopify, Klaviyo); implement attribution, CLV, and analytics models; ensure data quality, monitoring, and idempotent retry-safe pipelines.

Design, build, and maintain scalable data platforms and pipelines using Azure Fabric, Databricks, and Snowflake. Implement ETL/ELT (dbt), optimize workflows, ensure security and compliance, support batch and real-time ingestion, and collaborate with analysts, scientists, and stakeholders to deliver production-grade data models and analytics-ready data.

Design, build, and manage Northland's Azure-based enterprise data platform. Lead data architecture, develop scalable ETL/ELT pipelines, model enterprise data, ensure data quality and governance, deliver self-service data marts and Power BI datasets, and provide technical leadership to support analytics and AI initiatives.

Design, develop, test and support finance data solutions using Azure, Databricks, Python and Spark. Lead technical design, implement metadata-driven data pipelines and Bronze/Silver/Gold data lake patterns, support CI/CD DevOps processes, coordinate with finance stakeholders and vendors throughout implementation and delivery.

Design and maintain data architecture and pipelines to support compliance and risk teams. Build and optimize data models, standardize metrics, and create data dictionaries. Implement data quality, lineage monitoring, AI-driven agents for false-positive reduction and automation, and participate in on-call rotations to ensure SLAs are met.