Top Data Engineer Jobs in Toronto

Define the architecture and technical direction of Coalition’s enterprise data platform. Build scalable Snowflake and dbt infrastructure, governed data models, batch and streaming pipelines, reusable data products, feature stores, semantic models, and AI data foundations. Establish standards for data quality, security, lineage, observability, and regulated workflows. Partner across engineering, product, underwriting, actuarial, security, and business teams while mentoring engineers and guiding cross-team architecture.

Build and scale real-time and batch data pipelines, data platforms, and analytics-ready datasets using Kafka, Databricks, Snowflake, Python, and SQL. Design modern data models, ensure data quality and observability, develop AI-powered data experiences, and support business-critical reporting and analytics. Collaborate cross-functionally, improve platform performance, champion engineering best practices, and mentor teammates.

Designs and manages scalable data integration pipelines, governance controls, semantic enrichment, and orchestration systems for enterprise AI and analytics. Builds real-time data flows, APIs, connectors, RAG and embedding pipelines, vector database integrations, monitoring, audit trails, and AI guardrails. Partners with engineers, governance teams, and business stakeholders to make enterprise data secure, explainable, reusable, and ready for agentic AI workflows.

Own, build, and optimize Spark-based production data pipelines and ML data lake on GCP Dataproc to support recommender systems. Collaborate with ML engineers, resolve performance bottlenecks, and deliver scalable, reliable data products for AI-driven personalization.

The Senior Data Engineer will design and maintain streaming data pipelines, develop analytical databases, and ensure data system reliability while collaborating with teams across the organization.

Designs, develops, and supports scalable enterprise data warehouses, data lakes, cloud data platforms, and ETL/ELT pipelines. Responsibilities include SQL development, Snowflake and Databricks engineering, Spark and Python programming, data modeling, source-to-target mapping, testing, reconciliation, performance tuning, production support, and cloud modernization. The role translates business and technical requirements into reliable data solutions and collaborates with architects, analysts, application teams, and reporting stakeholders.

Designs and maintains Azure-based data lakehouse architecture, batch and real-time ETL pipelines, and data migration workflows from EHR, legacy, and business systems. Builds OLTP and OLAP data models, integrates Salesforce Health Cloud, transforms HL7, FHIR, and EDI data, and implements validation, deduplication, encryption, masking, and PHI-compliant security. Develops high-performance pipelines using Informatica, Azure services, Python, SQL, Spark, and related technologies to support clinical analytics, business intelligence, and AI initiatives.

Designs, builds, and operates scalable batch and streaming data pipelines and cloud data infrastructure using Databricks and related technologies. The role focuses on data modeling, orchestration, quality checks, lineage, monitoring, access controls, and reusable engineering frameworks. The engineer partners with Data Scientists, ML Engineers, and platform teams, contributes to architectural decisions and technical standards, troubleshoots production issues, reviews code, documents practices, and mentors junior engineers.

Design, build, and deploy scalable, secure data solutions on-premise and in Azure. Collaborate with engineering, security, and ops to implement modern data practices (Databricks, Snowflake, Python, SQL), produce solution designs, support project delivery, and promote DevSecOps and cloud best practices.

Leads the architecture, development, optimization, and operation of large-scale batch and real-time data platforms using Spark, Databricks, Kafka, and lakehouse technologies. Designs petabyte-scale pipelines, tunes Spark and SQL performance, manages governance and monitoring, establishes data-modeling and operational standards, and leads a global data engineering team. Advises stakeholders, mentors engineers, manages delivery risks and dependencies, and ensures production readiness, data quality, compliance, and support.

Leads the architecture, development, reliability, governance, and scalability of batch and real-time data platforms. Designs pipelines, self-service tooling, observability frameworks, and disaster recovery practices; mentors data engineers; establishes engineering standards; and partners cross-functionally to enable trusted data access for analytics, data science, and product teams. The role also contributes to strategic roadmaps, platform innovation, technical debt reduction, and on-call support.

Lead design, development, testing, implementation and support of data and AI products. Translate business requirements, provide technical direction, mentor developers, build automation and frameworks, improve delivery processes, and perform data analysis, profiling and regression testing.

Build and operate Block’s data platform, including warehousing, orchestration, business intelligence, governance, and agentic workflows. Develop reliable Python services, developer tools, and Terraform infrastructure for petabyte-scale data. Partner with data practitioners and product teams, establish engineering standards, provide technical direction, and mentor engineers while ensuring security, reliability, observability, usability, and data governance.

Build and operate a scalable data platform supporting warehousing, orchestration, business intelligence, governance, and agentic workflows. Develop Python services, Terraform infrastructure, and developer tools for petabyte-scale data. Partner with data practitioners and product teams, establish security and reliability patterns, set technical direction, improve engineering standards, and mentor engineers.

The Data Engineer is responsible for designing and optimizing data pipelines, ensuring data integration, and collaborating with teams on AI-driven applications.

As a Data Analyst, you will analyze user behavior, create interactive dashboards, and collaborate with teams to provide actionable insights for product improvement.

Lead and develop a team of data engineers building scalable real-time and batch pipelines and cloud data platforms. Set technical direction, manage major initiatives, and establish practices for architecture, testing, observability, reliability, and operational excellence. Partner with Product, Data Science, Analytics, and Engineering teams to deliver trusted data solutions supporting analytics, experimentation, machine learning, personalization, and high-volume marketplace experiences.

Design, build, and operate scalable real-time and batch data pipelines using Kafka, Spark, Flink, Airflow, Databricks, AWS, and dbt. Implement data quality frameworks, optimize distributed workloads, and develop reliable cloud-native data platforms. Own initiatives from architecture through production support while collaborating with Data Science, Product, Analytics, and Engineering teams. Create reusable engineering patterns, improve system scalability and performance, and maintain documentation for critical data systems.

The Principal Data Engineer at RAVL will design and implement enterprise-grade data and AI platforms, mentor engineers, and influence architectural standards across teams.

The Data Engineer will design and manage data pipelines and architectures, ensuring data quality and security while collaborating with cross-functional teams for effective analytics and reporting.

The Data Engineer will analyze and interpret data to drive insights, create dashboards, and enhance product performance, collaborating with cross-functional teams.

Looking for a Data Engineer experienced in Dataiku to design and maintain data pipelines and infrastructure, ensuring data flow and integration across teams.

The Data Engineer will architect and implement advanced analytics capabilities, manage data pipelines, and optimize data processing using AWS cloud and various analytical tools.

Build and operate Ground News’s data platform, including warehouse integrations, scalable dimensional models, analytical layers, and KPI infrastructure. Own ingestion pipelines, data quality, monitoring, incident response, infrastructure as code, and deployment workflows across AWS and GCP. Partner with analysts, ML engineers, and product stakeholders to create reliable, reusable data models and ensure accurate, observable, cost-effective data systems.

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.