Top Data Engineer Jobs in Montreal

Build and scale reliable Snowflake data pipelines, ML pipelines, monitoring systems, and AI-driven agentic tooling that automates pipeline generation, testing, and maintenance. The role partners with analysts, data scientists, product teams, and analytics engineers to deliver scalable end-to-end data infrastructure for Babylist’s e-commerce platform.

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.

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 data modeling and ownership of critical pipelines supporting Block’s product data foundation. Build reliable, governed, and monitored datasets, data quality and lineage systems, experimentation infrastructure, and AI-assisted automation. Partner with product and engineering teams to translate business needs into end-to-end data solutions, participate in on-call support, and maintain pipeline SLAs.

Build and optimize data models, pipelines, monitoring, lineage, and data quality systems supporting Block’s product data foundation. Own critical data engineering solutions across their full lifecycle, ensure pipeline reliability through on-call support, and translate business and product needs into automated data solutions. Develop AI-assisted workflows and agents for data quality, issue resolution, and operational efficiency while partnering with engineering, product, data science, and machine learning teams.

Designs and maintains data pipelines and ETL processes in Microsoft Fabric, ensuring data quality, governance, warehouse performance, and reliable migration of data assets. Collaborates with stakeholders to develop scalable cloud data solutions, resolves pipeline issues, and supports analytics and reporting needs.

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.

Design, build, and support production data feeds and integration pipelines using Python, SQL Server, Snowflake, REST APIs, SFTP, Docker, and AWS ECS. Develop incremental-loading workflows, apply Data Vault modeling, troubleshoot end-to-end data pipelines, and implement validation, monitoring, logging, error handling, and retries. Collaborate with technical and business stakeholders while maintaining pipeline and operational documentation.

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.

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 own Snout’s data foundation, including PostgreSQL and dbt warehouses, ingestion pipelines, dimensional models, data quality, documentation, and reliable rebuilds. Reconcile data from veterinary practice-management systems, support LLM/AI tooling, translate stakeholder needs into technical plans, and establish the foundation for a future data team and analyst.

Build and operate platform infrastructure for product analytics, data processing, and ML systems. Responsibilities include event tracking pipelines, Databricks Bronze-to-Gold lakehouse layers, schema evolution, data contracts, CI validation, backfills, monitoring, feature computation, model pipelines, lineage, PHI policy enforcement, and self-service developer tooling. The role focuses on making data and ML changes safe, observable, reproducible, and accessible to engineers and subject-matter experts.

Design, build, and maintain data pipelines and infrastructure (with strong Foundry focus). Optimize data quality, collaborate in cross-functional agile teams, support pre-sales and deliver client-facing technical solutions, and help establish Unit8's Canadian presence.

Design, build, and operate scalable data platforms covering ingestion, orchestration, modeling, quality, observability, governance, and BI enablement. Develop batch and streaming pipelines with Airflow, Snowflake, dbt, Terraform, and CI/CD tooling. Establish engineering standards, data access controls, and AI-assisted workflows while enabling software, analytics, and non-technical users to build production-grade data products. Provide technical leadership, vendor management, documentation, training, and full DevOps ownership.

Build and own Novisto’s analytics data foundation, including lakehouse architecture, ingestion pipelines, curated datasets, data models, and reliable ETL/ELT workflows. Develop data quality, observability, testing, CI/CD, and incident-response capabilities. Design APIs and backend services for secure, multi-tenant data serving. Partner cross-functionally with engineering, product, and ESG data teams while mentoring developers and balancing scalability, cost, freshness, and maintainability.

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.

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.

Define the architecture and technical direction of Coalition’s enterprise data platform. Build scalable Snowflake and dbt infrastructure, governed data models, batch and streaming pipelines, reusable data products, feature stores, semantic models, and AI data foundations. Establish standards for data quality, security, lineage, observability, and regulated workflows. Partner across engineering, product, underwriting, actuarial, security, and business teams while mentoring engineers and guiding cross-team architecture.

Build and scale real-time and batch data pipelines, data platforms, and analytics-ready datasets using Kafka, Databricks, Snowflake, Python, and SQL. Design modern data models, ensure data quality and observability, develop AI-powered data experiences, and support business-critical reporting and analytics. Collaborate cross-functionally, improve platform performance, champion engineering best practices, and mentor teammates.

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.

Design and lead enterprise data platforms, including scalable lakes, warehouses, batch and real-time pipelines, governance, quality, monitoring, and modernization. Build data infrastructure for production AI/ML systems, including embeddings, vector stores, RAG preparation, feature stores, and training and inference flows. Partner with leadership and cross-functional teams on data strategy, establish engineering standards, make architectural decisions, and mentor engineers.

Build and operate Ground News’s data platform, including warehouse integrations, scalable dimensional models, analytical layers, and KPI infrastructure. Own ingestion pipelines, data quality, monitoring, incident response, infrastructure as code, and deployment workflows across AWS and GCP. Partner with analysts, ML engineers, and product stakeholders to create reliable, reusable data models and ensure accurate, observable, cost-effective data systems.

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.

Designs, builds, and operates enterprise data lakes, warehouses, lakehouses, ETL/ELT pipelines, databases, and reporting platforms. Prepares governed, model-ready data for AI and machine learning, supports feature stores and embedding workflows, administers relational databases, optimizes queries and dashboards, and ensures data quality, security, availability, and disaster recovery. Collaborates with technical and business teams, reports to senior management, and mentors data engineers.

Build and maintain reliable data pipelines, integrations, APIs, and event-driven or batch processing systems. Transform data from advertising, commerce, and order management platforms while improving monitoring, data freshness, performance, security, and scalability. Partner with product, infrastructure, and data engineering teams to expand the integration platform and onboard new sources in a cloud-native, multi-tenant environment.