Top Data Engineer Jobs in Montreal

Build and maintain Snowflake-based data pipelines and dbt models, orchestrated with Airflow. Implement Snowflake Secure Data Sharing, Snowpipe, EventBridge integrations, Terraform IaC, observability, and data governance to deliver anonymized partner-facing datasets and support eligibility, reporting, and reimbursement workflows.

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.

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.

Build and maintain ETL/ELT pipelines and orchestrations (Apache Beam, Snowpark, Airflow, Google Workflows). Manage data platforms (BigQuery, Snowflake, PostgreSQL, GCS), implement data validation, CI/CD, testing, monitoring, and performance/cost optimizations. Collaborate with engineers, product, and data scientists to deliver scalable, production-ready datasets for B2B products.

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 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 scale real-time and batch data infrastructure and pipelines (Kafka, Databricks, Snowflake) to power analytics, reporting, and AI-powered data experiences. Drive data modeling, governance, observability, and best practices while partnering with cross-functional teams and mentoring engineers.

Join a talent network connecting data engineers with VC-backed startups. Build scalable batch and real-time pipelines, data models, warehouses/lakehouses, ensure data quality, support ML pipelines, integrate diverse data sources, and define data architecture and governance.

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.

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.

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.

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.

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

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

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.

Design, build, and deliver secure, efficient data pipelines and transformations using Databricks, AWS, Snowflake, Python and PySpark to provide ML/GenAI-ready datasets. Participate in analysis, implement best practices, and provide technical leadership within an Agile team.

Design, build, and operate scalable ETL/ELT pipelines and lakehouse schemas for multi-tenant SaaS data. Implement CDC and near-real-time ingestion, ensure data quality, governance, and tenant isolation. Collaborate with Product, Engineering, and DevOps to deliver reliable, performant pipelines; mentor teammates and shape the data platform roadmap to power client-facing and analytics capabilities.

Design, build, and maintain scalable cloud data pipelines and architectures. Develop and optimize ETL/ELT workflows, manage data warehouses (primarily BigQuery), implement governance and security, collaborate with engineers and analytics teams, enable ML/AI use cases, and maintain documentation, lineage, and high-availability pipelines.

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.

Build and operate a cloud data platform on Databricks and AWS: provision infrastructure with Terraform, manage Helm/Kubernetes and Docker-based deployments, implement CI/CD and artifact pipelines, and design Databricks jobs and Spark Structured Streaming pipelines for ETL/CDC across Bronze/Silver/Gold layers while tuning for cost and performance and collaborating in Agile teams.

Build and operate production lakehouse pipelines and reusable data products on Snowflake and dbt (with Databricks where appropriate). Partner with product and engineering to translate requirements, ensure data quality/governance, enable self-service, and support customer-facing reporting. Drive modernization, metadata management, performance tuning, and AI-assisted development workflows.

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.