Top Data Engineer Jobs in Montreal

Designs and maintains reliable data pipelines and computed tables using SparkSQL and PySpark. Integrates IoT, product, customer, external, unstructured, and ML-related data into scalable data models supporting analytics, causal inference, model training, and dashboards. The role requires ETL development, data orchestration, API integration, strong data quality and uptime, and collaboration with Data Science, AI/ML, Analytics, and engineering teams. This is a remote position for candidates residing in Canada.

Architect and own marketing data infrastructure, including Databricks pipelines, Gold Layer tables, semantic models, and trusted analytics foundations. Lead cross-functional data initiatives, translate ambiguous business needs into scalable solutions, improve reliability and quality, and mentor engineers. Build and monitor production LLM and agent systems, automate workflows with AI, and deliver high-quality Python and SQL while validating AI-generated code. Partner with Marketing, Sales, BI, Data Science, and R&D teams.

Build and maintain FinOps data pipelines, BigQuery and dbt models, cost attribution systems, forecasting and anomaly detection models, and cost-versus-revenue analytics. Identify and implement AWS and GCP savings opportunities, automate governance with Python, Terraform, and Lambda, and partner with Engineering, Finance, Product, and leadership on cloud costs, forecasting, reporting, and architecture decisions.

Lead product data engineering across architecture, data modeling, pipelines, monitoring, lineage, and data quality. Build reliable analytical datasets, support experimentation and machine learning use cases, and resolve business-critical pipeline issues. Partner with product and engineering stakeholders to translate business needs into automated data solutions. The role requires ownership of end-to-end data engineering projects, on-call participation, and development of AI-assisted monitoring and automation.

Lead product data engineering initiatives by designing data models, owning critical pipelines, building data quality and lineage monitoring, and supporting experimentation and ML-ready datasets. Translate business needs into automated data solutions, maintain pipeline SLAs through on-call support, and develop AI-assisted tools for issue detection and resolution. Collaborate with Product, Engineering, Data Science, Machine Learning, and Applied AI teams across Block’s products.

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.

Own and evolve WorkOS’s internal data platform, including ingestion, orchestration, Snowflake, dbt models, data governance, reverse ETL, semantic views, and AI-enabled data workflows. Ensure pipeline reliability, freshness, security, and scalability while partnering with Product, Finance, RevOps, GTM, Engineering, and Security. Build monitoring, runbooks, CI/CD gates, access controls, masking policies, and infrastructure automation across AWS and Kubernetes.

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.

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.

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 own production-grade data pipelines, source connectors, warehouse transformations, curated data models, reconciliation, and lineage for an AI-native finance platform. Develop dbt projects, orchestrate reliable and rerunnable workflows with Airflow or Dagster, tune warehouse performance, integrate messy enterprise systems, and ensure data correctness, auditability, and tenant isolation. Collaborate with backend and AI teams, establish data contracts, review work, and mentor engineers.

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.

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.

Lead the architecture and hands-on engineering of Cloudbeds’ Snowflake and dbt data platform. Own data modeling, warehouse design, ingestion, orchestration, DataOps, CI/CD, governance, observability, performance, cost optimization, and AI/ML readiness. Mentor analytical engineers, evaluate emerging tools, establish data quality and security standards, and collaborate with engineering, infrastructure, data science, and business stakeholders in a fully remote environment.

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, 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.