Top Data Engineer Jobs in Montreal

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.

Designs, develops, and optimizes scalable data ingestion, transformation, and analytics pipelines. The role supports Azure Databricks datamarts, cloud migration, legacy modernization, data quality, modeling, and performance improvements. It collaborates with analysts, developers, data scientists, architects, and Power BI specialists while contributing to Agile planning and technical reviews. Requires strong Python, Spark, PySpark, SQL, Azure data platform, CI/CD, data warehousing, and modern cloud architecture experience, plus fluency in French and English.

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.

Concevoir, développer et industrialiser des plateformes de données modernes sur AWS. Le rôle couvre l’ingestion, la transformation, l’orchestration, les architectures Data Lake, Data Warehouse et Lakehouse, ainsi que l’optimisation des performances, coûts, qualité, sécurité et gouvernance. La personne collaborera avec des équipes pluridisciplinaires, participera aux pratiques DataOps, DevOps et CI/CD, contribuera aux choix d’architecture et accompagnera les membres juniors. Cinq ans d’expérience minimum en Data Engineering, Big Data ou intégration de données sont requis.

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.

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.

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 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, 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.

Design and optimize data pipelines, cleanse and transform datasets, and build reliable reporting and analytics solutions using PostgreSQL, Metabase, and related tools. Collaborate with stakeholders to define KPIs, develop dashboards, troubleshoot data processes, and deliver actionable insights that support data-driven decisions.

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 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.

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.

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 operate scalable AWS-based data infrastructure, pipelines, warehouses, and ETL processes. Maintain data quality, security, reliability, monitoring, and cost efficiency while supporting analytics, finance, dashboards, and reporting. Translate cross-functional requirements into durable data solutions, troubleshoot complex issues, manage Terraform infrastructure, and establish engineering standards. The role also mentors junior team members and improves data platform capabilities in a remote fintech environment.

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.

Leads architecture and development of large-scale batch and real-time data platforms and pipelines. Ensures data quality, governance, reliability, observability, scalability, and cost efficiency while building self-service capabilities. Supports analytics, machine learning, and generative AI teams with trusted data infrastructure. Establishes engineering standards, mentors engineers, and drives platform health across multiple teams. The role requires expertise in data integration, distributed processing, data modeling, cloud platforms, CI/CD, and infrastructure automation.

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.

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.

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.

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.

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.