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 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 operate large-scale Spark/PySpark data pipelines and the enterprise data platform. Implement data integrations, transformations (Python/SQL), monitoring, and data quality. Build MCP servers and AI-enabled tooling, optimize performance, mentor engineers, and lead cross-functional data projects to enable analytics and automation.

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 reliable, high-performance ETL/ELT data pipelines. Translate product analysis into technical specs, design data models, monitor data quality, create test documentation, and ensure production stability while aligning data solutions with product strategy.

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.

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.

Ensure health, stability, and performance of production data platform: respond to incidents, monitor and alert, manage pipelines and APIs, enforce data validation and quality checks, optimize processing performance, collaborate on AWS infrastructure, maintain runbooks/documentation, and support downstream data consumers across multiple business data sources.

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.

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 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.

Lead design and implementation of a federated Lakehouse data platform, unify data silos, define governance/observability, enable secure AI/ML (RAG/vector readiness), mentor architects/engineers, and present roadmaps to executives.

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.

As a Data Engineer, you will develop and manage data pipelines, ensuring reliable data ingestion and delivery while optimizing for simplicity and performance.

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

Lead design and delivery of Azure-based data platforms and Data Lakehouse architectures. Architect Microsoft Fabric reporting, build scalable ETL/ELT pipelines, optimize SQL/Power BI semantic models, enforce data governance (PII, multi-tenant, retention), migrate legacy systems, mentor engineers, and create AI-ready pipelines feeding embeddings, vector stores, and feature stores.

Provide production support and stabilization for an Azure-based analytics platform, develop and maintain ETL/ingestion pipelines to data lakes, implement dimensional models and data warehouse loading, perform unit testing and CI activities, monitor and resolve production anomalies, document processes, and support release deployments until handover to operations.

Design and build automated tests and observability checks for Databricks-based data pipelines. Validate data across Bronze/Silver/Gold layers using SQL and Python, integrate tests into CI/CD, investigate quality issues, and collaborate with developers and product owners to ensure trusted data for analytics and AI.

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.

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 Engineer will lead the design and development of scalable data infrastructure for analytics and AI capabilities, ensuring compliant data processing systems and improving the data platform.