Top Data Engineer Jobs in Montreal

Designs, develops, and maintains robust data ingestion and processing pipelines in Databricks. Responsibilities include ETL scripting, data cleaning, pipeline monitoring and optimization, data lineage and quality assurance, storage solution deployment, and engineering documentation. Requires at least five years of data engineering experience, strong Python and SQL skills, and knowledge of lakehouse concepts and ETL tools. dbt and cloud platform experience are preferred.

Own Treasury’s data architecture and technical roadmap, personally building and operating a Snowflake liquidity datamart with source integrations, data models, reconciliation, controls, and data contracts. Partner with machine learning engineers on forecasting datasets and lead the Treasury Management System build-versus-buy recommendation. The role requires production reliability, automation, incident response, stakeholder management, and translating ambiguous financial business problems into adopted systems.

Own Treasury’s data architecture and hands-on development of a Snowflake liquidity datamart. Build integrations, data models, reconciliation logic, data quality controls, forecasting datasets, and production pipelines. Partner with Treasury, Accounting, and machine learning engineers; lead Treasury Management System build-versus-buy recommendations; and operate reliable, controlled data systems through adoption. The role also includes automation, monitoring, incident response, and reducing manual reconciliation and investigation.

Build and maintain scalable data pipelines, integrations, and data models connecting product, customer, sales, billing, and revenue data. Partner with Sales, RevOps, Finance, and Product to support forecasting, reporting, customer intelligence, segmentation, retention, and expansion analysis. Implement data validation, monitoring, documentation, and governance while improving infrastructure and automation for the company’s growing B2B operations.

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.

Leads data engineering consulting, modern Lakehouse platform architecture, and development of scalable batch and real-time data pipelines across Azure, AWS, and GCP. Builds and manages a data engineering team, defines architecture and security practices, supports AI presales, develops accelerators, and engages clients and stakeholders. Requires deep expertise in Python, Spark, Databricks, cloud data platforms, streaming, orchestration, CI/CD, and data architecture, plus bilingual French and English communication skills.

Designs and maintains cloud data warehouses, pipelines, models, integrations, and migration solutions. Collaborates with business and technology stakeholders to deliver scalable data platforms supporting reporting, analytics, machine learning, and data activation. Ensures data quality, governance, documentation, and operational reliability while contributing to the enterprise data roadmap and mentoring team members.

Own EULER’s data lifecycle, including instrumentation, validation, reconciliation, privacy, anonymization, canonical data modeling, analytics datasets, documentation, and production pipelines. The role requires strong SQL, software engineering practices, independent ownership, skepticism toward data quality, and clear communication. Preferred experience includes GDPR/LGPD, multi-tenant B2B SaaS, dbt, statistics, Cloudflare Workers, Postgres, Parquet, DuckDB, and Bubble.

Develops, tests, implements, documents, and maintains ETL data migration solutions for onboarding healthcare clients. Defines customer needs, designs data transfer interfaces, reviews code, identifies bugs, supports multiple projects, establishes development environments, and coaches junior developers. Requires strong C#, SQL, data integration, database, communication, problem-solving, French, and English skills.

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 the design and implementation of Agiloft’s contract data model, including scalable pipelines, data warehouses, analytics layers, governance, monitoring, and quality controls. Drive architecture decisions, schema redesigns, and migrations in a multi-tenant SaaS environment while collaborating with Product, Engineering, Data Science, ML, and customer-facing teams. Provide technical mentorship and guide multi-quarter data initiatives supporting AI, analytics, and contract intelligence.

Build and maintain financial data infrastructure supporting accounting and reporting. Translate finance requirements into scalable data solutions, ingest and transform ERP data, develop Fabric lakehouses and warehouses, maintain Power BI semantic models, and consolidate general ledger, accounts payable, accounts receivable, and asset-management data. Partner with accounting, finance, analytics, and architecture stakeholders to ensure data quality, governance, consistency, and availability.

Design and deliver large-scale data pipelines and cloud migration solutions using Azure Synapse Analytics. Build and optimize ETL/ELT workflows processing terabytes or petabytes of structured and unstructured data. Develop automation, monitoring, testing, storage, and compute tooling across Azure environments. Collaborate with infrastructure, engineering, DevOps, and cloud teams while mentoring engineers and leading large projects.

Own and operate the company’s Microsoft Fabric data platform and DOMO reporting environment. Build lakehouses, pipelines, models, monitoring, and governed datasets for analytics and AI agents. Develop MCP and API connectors with secure permissions and logging, improve architecture and control costs, and ensure data quality, lineage, privacy, and compliance. Work independently in production environments using Git, Bitbucket, and Azure DevOps while collaborating with technical and nontechnical teams.

Develop and maintain ETL/ELT data pipelines and data infrastructure within a cloud-centric data lakehouse environment. Responsibilities include pipeline design, development, testing, documentation, operational support, business requirements analysis, proof-of-concept implementation, reporting, and BI semantic-layer support. The role requires advanced SQL and Python/PySpark expertise, cloud and database experience, knowledge of Delta Lake, Spark, Parquet, data architectures, and privacy and security compliance.

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.

Own and improve Life360’s Ads Data Engineering platform on Databricks. Build, operate, and optimize batch and streaming pipelines, Delta Lake tables, dbt models, semantic layers, and core metrics. Integrate advertising and business data, implement quality and governance controls, manage AWS infrastructure with Terraform, reduce compute costs, and mentor engineers. Partner with analysts, ad strategists, and sales teams to deliver trusted data products while developing AI-assisted engineering tools and workflows.

Build and maintain large-scale data models, batch and streaming pipelines, and shared datasets supporting product reporting, customer insights, machine learning, and AI agents. Improve data quality, reliability, performance, testing, monitoring, and maintainability across Snowflake and Databricks platforms. Collaborate with product engineering to define user interaction data models and lead complex data engineering initiatives.

Performs ETL data migration from legacy utility billing systems into SpryPoint’s platform. Develops Python and SQL scripts, profiles source databases, maps and transforms datasets, validates data quality, and supports reliable go-live integrations. Collaborates with technical and client teams, documents best practices, improves automation and observability, and participates in iterative reviews. This early-career role emphasizes data conversion, auditing, process optimization, and continuous learning.

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.

Lead Cohere’s Data Foundation pillar, managing a team responsible for the full data lifecycle. Own the roadmap, BigQuery warehouse, Airflow and Cloud Run orchestration, dbt modeling, Looker and API serving, Terraform infrastructure, governance, access controls, reliability, incident response, and data freshness SLAs. Lead development of an agentic analytics platform and evaluation framework, establish company-wide analytics standards, and partner with product, engineering, security, legal, and business teams.

Own data engineering for workforce forecasting and staffing products. Build and operate Databricks pipelines, lead the Redshift-to-Databricks migration, manage database replication and file ingestion, establish data contracts, handle PII and data residency, monitor quality and costs, and support on-call operations. The role also requires documentation, infrastructure-as-code, collaboration with product and analytics teams, and pipelines supporting forecasting or machine-learning models.

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.