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.

Design, build, and automate scalable ETL pipelines to centralize disparate studio data; standardize and model data for executive BI dashboards; partner with product and studio leaders to resolve data bottlenecks; implement automated data-validation frameworks to ensure reliable leadership reporting.

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 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.

Design, build, and maintain scalable data ingestion pipelines, databases and data lakes for credit and lending datasets. Ensure accurate data mappings, monitoring and alerting, translate stakeholder requirements into analytical data flows, and integrate APIs to deliver reliable, high-quality data products.

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.

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.

The Senior Data Platform Engineer at 1Password will design, build, and maintain data systems and streaming architectures, ensuring data quality and governance for real-time access.

Design, develop, and maintain ELT/ETL Snowflake data warehouse solutions; implement data standards, CI/CD, monitoring, and production support; collaborate with product, business, and vendors; mentor junior developers and improve data engineering practices.

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 build an AWS-based lakehouse and scalable enterprise data platform. Create governed data products and automated Python/SQL pipelines. Establish standards for data quality, governance, security, observability, and CI/CD. Partner with engineering, product, finance, and business teams to enable analytics and AI and define the platform's long-term vision.

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.

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 Staff Data Engineer will design and implement scalable data pipelines, mentor junior engineers, and enhance data system performance and architecture.

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

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

Design and improve game data models and ETL pipelines using SQL, dbt, Snowflake, Python, and Airflow. Implement testing, security, cost and performance monitoring, promote data governance and best practices, lead data projects, and mentor junior team members to support analytics and user research for live game operations.

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.

Build, own, and evolve Flinks' data and ML platform: BigQuery/dbt pipelines, Airflow ingestion, Kubeflow/Vertex training, model serving, governance, observability, and cross-team data contracts to support production ML and analytics at scale.

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

Lead design, build, and operate scalable data platform and pipelines (ETL/reverse ETL). Own architecture for AWS/Snowflake data lake, optimize ingestion, enforce data quality and governance, introduce AI-driven engineering practices, mentor engineers, and drive cross-functional data projects from requirements through production.

Design, build, and maintain company-wide Snowflake data assets and dbt data models. Define metrics and dimensions, implement reliable pipelines, enable self-service analytics, collaborate with stakeholders, ensure data quality, and document solutions to support reporting, dashboards, and analysis.