Data Engineer

• Design, build, configure, and operate cloud-based data and analytics platforms supporting BI, advanced analytics, data engineering, and AI/ML workloads
• Lead platform lifecycle management, including onboarding, upgrades, scaling, decommissioning, and continuous improvement
• Develop and enforce platform usage, governance, and operational standards
• Implement and maintain monitoring, logging, alerting, and performance management
• Diagnose and resolve complex platform incidents, performance issues, and service disruptions
• Analyze tactical and operational information to diagnose platform and service issues and define technical solutions
• Ensure platform compliance with security, privacy, and data governance policies
• Approve and contribute to database and platform specifications
• Collaborate with data governance and risk partners on regulatory, audit, and compliance expectations
• Develop and maintain platform and technology roadmaps aligned with EDC’s data strategy
• Evaluate emerging technologies through proof of technology
• Drive modernization and rationalization of legacy data platforms
• Define and track platform performance, cost, reliability, and adoption metrics
• Define and evolve data and analytics reference architectures, standards, design patterns, and guiding principles
• Design end-to-end data solution architectures, including data flows, integrations, and platform services
• Conduct data exploration and analysis to identify architectural, operational, and performance trends
• Partner with cloud, security, integration, and solution architects on enterprise design outcomes
• Advise, mentor, and coach data engineers, architects, and platform administrators
• Collaborate with product owners and delivery teams to execute platform backlogs using Agile practices
• Document architectures, operational runbooks, standards, and lessons learned
• Provide technical leadership and communication to business and technology stakeholders

Senior Data Platform Administrator

• Design, develop, configure, and operate cloud-based data and analytics platforms
• Lead platform lifecycle management, including onboarding, upgrades, scalability, decommissioning, and continuous improvement
• Develop and enforce platform operational, usage, and governance standards
• Implement and maintain monitoring, logging, alerting, and performance management systems
• Diagnose and resolve complex incidents, performance issues, and service disruptions
• Analyze tactical and operational information to identify underlying issues and define technical solutions
• Ensure compliance with data security, privacy, and governance policies
• Approve and support database and platform specifications
• Collaborate with data governance and risk management teams to address regulatory, audit, and compliance requirements
• Develop and maintain platform and technology roadmaps
• Evaluate new technologies and emerging tools and services through proof-of-concept testing
• Reduce technical debt by modernizing and rationalizing legacy platforms
• Define and track platform performance, cost, reliability, and adoption metrics
• Define and evolve reference architectures, standards, design patterns, and guiding principles for data and analytics
• Design end-to-end data solution architectures, including data flows, integrations, and platform services
• Conduct targeted data exploration and analysis to identify architecture, operational, and performance trends
• Collaborate with cloud, security, integration, and solution architects
• Advise, mentor, and coach platform engineers, architects, and administrators
• Collaborate with product owners and delivery teams to plan, prioritize, and implement backlogs using Agile practices
• Document architectures, runbooks, standards, and lessons learned
• Provide technical expertise to stakeholders across technology and business teams

Lead Data Platform Administrator

• Lead development of performant, reliable, and maintainable data pipelines using Airflow and BigQuery
• Own pipeline architecture, design, testing, deployment, monitoring, and maintenance
• Make independent design decisions and operate the systems built
• Partner with data analysts and stakeholders to define requirements and design scalable data models and applications
• Own data products end to end, including pipelines, models, backend APIs, customer-facing dashboards, and applications
• Design and operate AI and agentic workflows that transform raw documents into structured data
• Develop prompts and context, tool/function calling, embeddings and retrieval, and structured outputs
• Manage production concerns including retries, idempotency, cost, and latency
• Own evaluation datasets, LLM-as-judge and rule-based QA checks, and benchmarks
• Use evaluation results to guide decisions about models, prompts, and architecture
• Continuously improve data infrastructure and processes
• Evaluate and introduce new technologies and best practices
• Help shape the use of AI coding agents for development, debugging, review, and documentation
• Perform other duties as assigned

• Architect, design, and develop end-to-end data pipelines using Azure Data Factory, Azure Databricks, and Azure Synapse Analytics
• Design and implement data models within Azure Data Lake using Medallion Architecture
• Ingest data from relational databases, APIs, flat files, and project management systems
• Automate data workflows and implement CI/CD practices using Azure DevOps or GitHub Actions
• Monitor, troubleshoot, and optimize data solutions for performance, scalability, and cost-effectiveness
• Identify and resolve data pipeline bottlenecks, partitioning issues, and cost-control challenges
• Develop and maintain internal Python tools and applications, including Generative AI modules, automation scripts, and data processing pipelines
• Build custom web-based dashboards and interactive reporting tools using HTML, CSS, JavaScript, Flask, Dash, or Streamlit
• Integrate AI-driven solutions into project advisory workflows
• Ensure data quality, integrity, governance, validation, and documentation
• Review and validate project schedules and budgets, forecast project performance, and perform project risk assessments and monitoring
• Assist with proposals and opportunity development for the Capital Projects & Infrastructure practice
• Collaborate with project advisors, data analysts, and business stakeholders
• Perform site visits and inspections as needed
• Work on site, including travel to remote locations when required

Senior Associate, Deals Real Assets, Data Engineering, Digital Solutions

• Architect how GitLab and third-party data is ingested, modeled, and synced into the knowledge graph as a near-real-time graph of the development ecosystem
• Lead integration with external systems for business context, including Jira, observability tools, Zendesk, and ServiceNow
• Architect the Data Marketplace so customers consume GitLab data through Snowflake, Databricks, and BigQuery without engineering dependencies
• Publish GitLab observability as OpenTelemetry and broker data-sharing APIs
• Define operational readiness for everything shipped across GitLab.com, Dedicated, and Self-Managed
• Translate an ambiguous 0-to-1 problem space into concrete, iterable roadmaps with Product, Design, and the graph backend team
• Resolve cross-team dependencies across graph backend, AI Platform, and Infrastructure
• Mentor senior and intermediate engineers through design reviews, pairing, and code review
• Identify systemic bottlenecks, simplify processes, and design systems that improve execution velocity

Staff Fullstack Engineer, Data Products – Golang / Node

• Formulate data and AI governance strategies aligned with business objectives and industry best practices
• Create, socialize, and implement data governance policies, procedures, and frameworks while ensuring compliance
• Introduce emerging technologies and methodologies for data governance tasks and capabilities
• Develop and execute data governance and/or AI governance strategies improving data discoverability, quality, accessibility, usability, AI use case evaluation, and AI readiness
• Lead migration of complex data architectures from on-premises to cloud environments
• Establish and enforce data governance practices improving data security and compliance
• Introduce containerization to enhance scalability and deployment efficiency
• Mentor engineers, review architectures and code, and guide teams through implementation
• Lead technical discovery, shape solution architectures, respond to RFPs, and deliver demos and proofs of concept for data engineering engagements
• Create technical blueprints and recommend tools, frameworks, and design patterns aligned with client needs
• Partner with sales and account teams to scope engagements, shape technical proposals, and showcase governance expertise

Architect, Data Governance

• Own the Databricks data lake end to end, including dbt models, medallion layers, incremental and backfill strategy, partitioning, freshness, and quality monitoring
• Stand up CDC and streaming ingestion from HubSpot, Langfuse, Postgres, and DynamoDB into the data lake, handling idempotency and deduplication
• Own data services, schema and migration strategy, versioned APIs, provenance and audit trails, tests, and observability
• Build AI data infrastructure, embedding pipelines, vector stores, retrieval knowledge bases, feature stores, and LLM observability
• Develop and maintain cloud databases with the Infrastructure team
• Improve data retrieval and optimize analytics dashboards
• Maintain data management and security policies
• Collaborate with software, AI/ML, and data engineers, data scientists, product, and business units to align requirements
• Communicate technical concepts clearly to non-technical stakeholders
• Guide and mentor engineers in data best practices

Senior Data Engineer

• Design and maintain backend systems and data infrastructure supporting high volumes of integrations and real-time events
• Contribute to backend and frontend development using Node.js, React, and Serverless frameworks
• Drive architecture decisions for the data pipeline and integrations platform
• Document technical standards that scale across the team
• Collaborate with architects, R&D, product, engineering, and cross-functional teams
• Participate in and help lead design and planning meetings
• Scope and break down future work with product and engineering stakeholders
• Manage and prioritize work in Jira and own deliverables through completion
• Troubleshoot and resolve issues for customer support and success teams
• Research new technologies and improve product performance, maintainability, and scalability
• Coach engineers through complex problems and contribute to code reviews
• Lead projects end-to-end and keep stakeholders informed
• Build frameworks, standards, and tooling that improve team effectiveness

Staff Software Engineer, Data

• Build and maintain ingestion and orchestration pipelines using Fabric Data Factory and Azure Data Factory
• Integrate databases, APIs, files, and enterprise applications
• Develop ETL/ELT transformations using SQL, Python, PySpark, Fabric notebooks, and Dataflow Gen2
• Implement OneLake, Lakehouse, Warehouse, and Delta Lake solutions using medallion architecture where appropriate
• Develop full and incremental load patterns, change data capture, parameterized pipelines, and reusable ingestion frameworks
• Build dimensional models and curated datasets with analysts and business stakeholders
• Implement business rules, historical tracking, and source-to-target mappings
• Implement data quality checks, automated tests, reconciliation, and validation
• Handle schema changes, duplicate records, missing values, and late-arriving data
• Improve pipeline reliability through logging, monitoring, alerts, retries, dependency management, and safe reruns
• Troubleshoot failures and document operational runbooks
• Use Git and CI/CD to promote changes across environments
• Optimize SQL, Spark, and pipeline performance
• Implement agreed access controls
• Participate in code reviews and mentoring
• Work closely with architects, analysts, and clients from source analysis and development through testing, deployment, and production support

Azure Data Engineer – Fabric

• Lead discovery workshops to understand business priorities, source systems, data volumes, security requirements, and operational constraints
• Translate discovery findings into practical architecture and phased implementation plans
• Design Microsoft Fabric environments, including tenant and capacity considerations, workspace organization, development and production separation, and ownership and support responsibilities
• Define OneLake architecture, Lakehouse and Warehouse patterns, medallion layers, data organization, shortcuts, mirroring, and data movement
• Select ingestion, transformation, and orchestration approaches across Fabric Data Factory, Dataflow Gen2, Spark notebooks, and relevant Azure services
• Design data models and curated data products for reporting, Power BI semantic models, analytics, AI, and machine learning use cases
• Establish security and governance with client teams, including Microsoft Entra ID, access controls, data ownership, lineage, classification, and Microsoft Purview integration
• Set standards for Git, CI/CD, deployment, testing, monitoring, recovery, and performance and capacity management
• Validate key architecture decisions through hands-on implementation
• Lead technical delivery and architecture reviews
• Mentor engineers
• Work with pursuit and delivery teams on scope, estimates, dependencies, and implementation risks

Data Engineer jobs in Canada