Top Data Engineer Jobs in Montreal

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.

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.

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.

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.

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.

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.

Lead technical implementation of a market management platform for nodal electricity markets. Design and operate large-scale, data-intensive production systems handling continuously changing data. Make architecture decisions, contribute production software, manage incidents, improve existing systems, and partner with product, engineering, data science, and power-market experts. Lead technical reviews, mentor engineers, and drive optimization, forecasting, and analytical capabilities.

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

Design, develop, and maintain MongoDB databases and Kafka clusters; perform DBA tasks, infrastructure engineering for stability and scalability, develop tools for SingleStore/Kafka/MongoDB, support deployments and provide Level 2 on-call support.

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.

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.

Develop and maintain scalable data warehouse models and curated datasets using dbt and Snowflake. Build batch and event-driven pipelines with Snowpipe and EventBridge, implement data quality monitoring and alerting, participate in technical design and code reviews, and champion data governance, anonymization, privacy, and access controls for sensitive health data.

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.

Build and operate production-grade data ingestion and transformation pipelines in Databricks using Spark/PySpark, SQL, Delta Lake, and medallion architecture. Manage schemas, data quality, deduplication, API integrations, orchestration, monitoring, alerting, recovery, and performance optimization. Collaborate with data scientists and application teams to provide reliable data for LLM/NLP pipelines. Contribute to CI/CD, documentation, data architecture, and onboarding new data sources.

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.

Lead the architecture and technical direction of a cloud data platform spanning infrastructure, Databricks, CDC streaming, pipelines, data products, developer experience, and governance. Resolve cross-team technical challenges, establish platform standards, influence roadmaps, drive adoption, mentor senior engineers, and lead complex multi-squad initiatives with minimal direction.

Designs, builds, and maintains scalable batch and real-time data pipelines supporting an IoT safety ecosystem. The role manages ETL/ELT processes, data lakes, warehouses, lakehouses, orchestration, monitoring, data quality, and observability. It partners with product, engineering, analytics, and data science teams to deliver reliable data services, productionize models, and shape long-term data platform architecture using AWS and modern infrastructure practices.

Design, build, and optimize Microsoft Fabric-based ingestion, transformation, and curated data layers. Migrate legacy sources, implement business rules, develop API-driven workflows, ensure data quality, security, and monitoring, and support CI/CD and operational delivery for analytics and reporting.

Build and maintain production-grade data processing systems and foundational infrastructure for enterprise AI. Collaborate with researchers and engineers, deliver end-to-end implementations, transform unstructured data into performant datasets, and use distributed processing frameworks and modern analytics tooling to inform products and strategy.

Design, build, and maintain reliable data pipelines and lakehouse assets for batch and streaming workloads. Implement ETL/ELT in Python and SQL, orchestration and monitoring (Airflow/Kubernetes), and retrieval/RAG/vector search pipelines for LLM and AI applications. Ensure data quality, lineage, versioning (Delta/Parquet/Iceberg), cost/performance optimization, and collaborate with MLOps and sports data teams.

Design, develop, and maintain scalable data pipelines, models, ingestion, transformation, and integration solutions using Databricks and Microsoft Azure. Troubleshoot performance and data quality issues, optimize workflows, support migrations and modernization, and collaborate with architecture and engineering teams. Translate business requirements into technical solutions while following security, governance, quality, and engineering best practices.

Leads the design and delivery of scalable data pipelines, dbt models, orchestration workflows, and analytical data models. Establishes data architecture, engineering standards, testing, observability, governance, and quality practices. Partners with stakeholders to deliver data-driven solutions, supports incident response, and provides technical leadership through design reviews, mentorship, hiring, and team development.