Top Data Engineer Jobs in Montreal

Build and maintain scalable ETL pipelines and databases to ingest, transform, reconcile, and deliver OTC derivatives data for regulatory, operational, and business reporting. Develop SQL objects, stored procedures, and automated reports; enforce data quality, governance, and security controls; participate in Agile delivery, CI/CD, and production support.

Build and maintain Java-based backend and scalable data pipelines while migrating legacy ETL from on-prem (Teradata) to cloud-native platforms (Databricks, Snowflake). Collaborate with engineering and architecture teams on modernization, performance tuning, testing, deployment, and production support for enterprise-scale distributed data systems.

Design, build, and maintain scalable ETL/ELT data pipelines and lakehouse architectures on GCP. Implement orchestration (Prefect), CI/CD, Terraform IaC, and DataOps practices. Collaborate with data scientists and product teams to enable analytics and AI while ensuring governance, security, and reliability.

Design, build, and maintain Snowflake-based data pipelines using dbt and Airflow. Own data modeling (dimensional modeling, SCDs), ingestion (batch and event-driven), data governance, observability, and tooling so teams can access reliable, documented analytics-ready datasets. Collaborate with analysts to define metrics and resolve quality issues.

Design, build, and maintain SparkSQL/PySpark data pipelines in the central data lake to ingest IoT, product, and unstructured data (video/audio). Produce reliable computed tables for analytics, model training, and dashboards; integrate external datasets; ensure high data quality and uptime; and collaborate with Data Science, ML, and cross-functional teams.

Design, build, and maintain scalable ETL/ELT data pipelines and data platforms (warehouses, lakes). Prepare and transform data for ML, deploy and industrialize models on cloud (Azure, Snowflake, Databricks), implement MLOps/DevOps practices (CI/CD, monitoring), collaborate with cross-functional teams, and document solutions for continuous improvement.

As a Growth Data Engineer, you will build and manage marketing data and attribution systems, ensuring reliable data flow and accurate measurement of acquisition efforts. The role involves collaboration across product, marketing, and data teams with a focus on optimizing campaign performance.

The Founding Data Engineer will design and build OpenArt's core data platform, ensuring data reliability, consistency, and scalability to support leadership and product metrics.

Build and operate scalable data ingestion, transformation, and connector frameworks; design and maintain a knowledge-graph-based data fabric; normalize and vectorize enterprise data for LLM/AI workflows; implement governance, lineage, access controls, and secure APIs to serve ML/agent pipelines.

The Guidewire Data Engineer will design schemas in Snowflake, develop ETL processes, optimize SQL queries, and integrate Guidewire with data platforms.

Design, build, and optimize cloud-based data platform components: ingest and transform varied datasets, create metadata-driven pipelines and data APIs, support AI/ML infrastructure, implement monitoring and metrics, and improve data processes to enable analytics and operational systems.

As an Intermediate Data Engineer, you will develop data infrastructure and products, collaborate with diverse teams, and enhance technical quality across the data team.

As a Sr. Data Engineer, you'll design and manage data pipelines, enhance data quality, and leverage AI to streamline operations, driving the success of our AI growth platform for real estate.

Lead design and implementation of shared, reusable data models and a certified metrics layer. Standardize pipeline patterns, CI/CD, and governance; modernize orchestration and observability; partner with Data Science, Infrastructure, and Product to deliver reliable analytics pipelines and enable AI-native data development.

The Analytics Engineer will design data models, build pipelines, and ensure data quality to support AI evaluation and insights.

Design, build, automate and maintain scalable ELT/ETL data pipelines and data architecture on cloud (AWS). Collaborate with analysts, data scientists and stakeholders to ensure data quality, governance, monitoring and performance. Apply ML-driven validation, anomaly detection, automated schema evolution and optimizations to support analytics and AI-native workloads.

The Senior Data Developer leads technical implementation of client data pipelines, ensuring reliable data integration, governance, and production readiness for health data sharing with partners across Canada.

The Founding Data Engineer will manage the company's data models, ensure metric integrity, and work with various teams to deliver reliable metrics and reporting systems.

The Senior Data Developer will enhance the scheduling system, building pipelines for ML models, ensuring reliability, and facilitating improvements for operational decision-making.

The Senior Data Engineer will lead the data engineering architecture and roadmap, design scalable ELT pipelines, model data, ensure data quality, and maintain the cloud infrastructure, collaborating closely with data experts and engineering teams.

Design, build, and optimize near real-time ELT/ETLT data pipelines and production-grade Python backend services. Implement software engineering best practices (testing, CI/CD, observability), develop analytical data models, collaborate with product stakeholders, integrate AI use cases, and mentor junior team members.

As a Data Engineer, you'll design and maintain data pipelines, model data for analytics, ensure data quality, and collaborate with teams to meet data needs.

Responsible for creating a marketing data platform by consolidating advertising data, developing data pipelines, and supporting marketing teams with analytics and solutions.

The role involves enhancing data platforms, designing data pipelines, implementing DevOps practices, managing data, ensuring compliance, and collaborating with teams.

Lead design and implement a scalable Medallion lakehouse on GCS and Databricks. Build and govern ingestion and transformation pipelines (Astro/Airflow, PySpark, Delta Live Tables), apply domain-driven data modeling, manage Unity Catalog access and lineage, collaborate with analytics and vendors, and proactively surface risks with clear documentation.