Top Data Engineer Jobs in Montreal

Build and operate production data pipelines supporting analytics, AI, and agentic workflows. Responsibilities include implementing canonical data models, maintaining Databricks or Snowflake platforms, monitoring reliability, responding to incidents, validating healthcare data mappings, improving performance and cost, and documenting architecture. Requires strong SQL and Python skills, cloud data platform experience, ETL/ELT orchestration expertise, and healthcare or pharmaceutical data experience.

Build and maintain scalable AWS data pipelines from source-system CDC through Bronze, Silver, and Gold lakehouse layers. Develop dbt transformations, data models, ETL processes, quality controls, catalog and lineage metadata, and reliability monitoring. Partner with analysts, product, GTM, AI teams, source owners, and customers to deliver governed data products. Troubleshoot incidents, optimize cost and performance, support AI-ready datasets, and contribute to new ingestion patterns and platform evolution.

Designs and develops scalable data processing solutions on Databricks and Azure. Responsibilities include building PySpark pipelines, designing Lakehouse architectures, integrating multiple data sources, optimizing performance and costs, automating testing and deployments, and supporting production data platforms. The role involves consulting with clients and collaborating with architects and analysts on cloud modernization, advanced analytics, AI, governance, and data quality initiatives.

Design and develop AWS data pipelines, ingestion, transformation, storage, and Data Lake/Lakehouse architectures. Optimize AWS data services, solution performance, costs, quality, security, and traceability. Collaborate with architects, developers, and business teams while contributing to automation, CI/CD, and data engineering best practices. The role supports varied consulting projects involving cloud modernization, advanced analytics, artificial intelligence, governance, and large-scale cloud data platforms.

Designs and optimizes scalable data pipelines and architectures supporting analytics and operational needs. The role develops ETL, streaming, data quality, governance, and cloud-based workflows using modern big data technologies. Responsibilities include translating business requirements into technical solutions, monitoring data systems, improving reliability and performance, and providing technical guidance on data modeling, storage, and metadata management.

Build and operate ETL and real-time pipelines connecting veterinary practice systems, billing platforms, and third-party APIs. Design data models and a warehouse supporting hospital analytics, finance, and reporting. Establish standards for testing, documentation, monitoring, and data quality, while troubleshooting production data issues. Partner with Finance to improve Sigma self-service reporting and define scalable integration patterns as the company’s first data engineer.

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.

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.

Build and operate scalable data pipelines supporting batch and streaming workloads, AI and ML systems, analytics, and sports intelligence products. Responsibilities include ETL/ELT development, data modeling, orchestration, retrieval pipelines, data quality, lineage, monitoring, infrastructure optimization, and collaboration with ML, product, and analytics teams.

Lead the technical direction of data engineering projects by designing scalable architectures, coaching data engineers, translating business needs into technical decisions, and contributing to critical code. Ensure delivery quality through testing, code reviews, validation, CI/CD, version control, and production deployment. Collaborate with AI, operations research, BI, and stakeholder teams while promoting effective use of AI tools and continuous team development.

Own Orum’s data platform architecture, roadmap, reliability, and scalability. Build batch and real-time ETL/ELT pipelines using PostgreSQL, event streams, ClickHouse, BigQuery, and dbt. Administer Looker and develop LookML models, dashboards, and a trusted semantic layer. Establish data quality, governance, lineage, observability, and metric standards while partnering with Product, Finance, GTM, and Operations on analytics, AI, and business intelligence initiatives.

Build and operate high-volume telemetry and analytics data pipelines supporting real-time monitoring, product decisions, experimentation, and user experience analysis. Design event instrumentation, schemas, quality checks, deduplication, late-event handling, and identity stitching. Partner with data science, product, and SRE teams to shape requirements, and integrate AI tooling into development and data quality workflows.

Design and operate scalable, cloud-native data pipelines and orchestration systems for imaging, biomarker, and healthcare data. Build Databricks and distributed processing workflows, integrate DICOM data into data lakes, and implement validation, observability, monitoring, and fault tolerance. Collaborate with data science, AI, R&D, product, and data management teams to deliver secure, compliant, reliable data for analytics and machine learning.

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.

Develop ETL data pipelines, scalable RESTful APIs, and microservices using cloud, big data, streaming, and database technologies. Optimize data processing and query performance, troubleshoot technical issues, ensure data integrity and security, write automated tests, review code, document APIs, and collaborate with engineering, product, analytics, infrastructure, QA, and business teams.

Architect, implement, scale, and maintain petabyte-scale data processing platforms for frontier AI models. Build reliable, observable, cost-efficient pipelines with heterogeneous and GPU-accelerated workloads, including failure recovery and restartability. Ensure datasets and intermediate outputs are versioned, reproducible, traceable, and documented with datasheets. Partner with AI researchers to integrate datasets into training pipelines and guide sustainable technology decisions.

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.

Owns end-to-end data infrastructure, including secure data lakes, streaming applications, data warehouse systems, replication and change data capture pipelines. Responsibilities include architecture, deployment, maintenance, performance optimization, security controls, documentation, operational runbooks, and cross-functional partnership with analytics and product teams. The role requires strategic data infrastructure thinking and production experience in SaaS or data-heavy environments.

Design, build, optimize, and maintain scalable Databricks data pipelines and ETL/ELT workflows. Manage CI/CD with Azure Pipelines and Azure Key Vault, improve performance and cost efficiency, implement data quality and governance practices, troubleshoot distributed systems, collaborate with stakeholders, and support cloud data platform modernization initiatives.

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 own scalable data pipelines, warehousing architecture, and dbt transformation layers from ingestion through analytics and incentive payouts. Lead data-platform architecture, including OLake, Iceberg, and Snowflake workflows. Establish performance, cost, quality, lineage, observability, and schema-drift standards. Partner with engineering and analysts on telematics accuracy and driver incentive reporting while using AI coding tools to accelerate development.

Own and scale the company’s analytics infrastructure, including BigQuery data warehouses, dbt data marts, external data exchange, DataOps, data quality, governance, security, and cloud cost optimization. Partner with application database teams, analytics engineers, and data scientists to enable reliable metrics and scalable client data delivery.

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

Lead the architecture and scaling of Overstory’s data platform for AI-powered vegetation analysis. Design and operate large-scale geospatial and temporal data pipelines, orchestration systems, event-driven workflows, service-oriented architecture, data quality frameworks, and observability practices. Drive architectural standards, system design, scalability, reliability, and traceability while mentoring engineers and collaborating with data, ML, and product teams.

Build and maintain production-grade dbt data models and pipelines across ingestion, transformation, and visualization. Partner with Finance, Engineering, Sales, Product, and Marketing to deliver reliable data for reporting and decision-making. Improve manual workflows, validate data accuracy, and design scalable solutions using modern data modeling practices. Support financial and revenue-related analytics, including ARR and revenue recognition, while balancing immediate fixes with long-term architecture.