About the Role
We are seeking an experienced Azure DevOps Engineer – ETL with strong expertise in Azure Databricks, Microsoft Fabric, Azure Data Lakehouse architecture, Delta Lake, and Parquet data formats.
The ideal candidate will have hands-on experience designing, automating, deploying, and supporting modern cloud-based data platforms and ETL/ELT pipelines within Microsoft Azure. This role requires a strong understanding of DevOps practices, data engineering, CI/CD automation, cloud infrastructure, security, monitoring, and release management.
The Azure DevOps Engineer will work closely with Data Engineers, Data Architects, Developers, BI teams, Cloud Engineers, and business stakeholders to ensure reliable, scalable, secure, and automated delivery of enterprise data solutions.
Key Responsibilities
- Design, implement, and maintain CI/CD pipelines for data engineering and ETL/ELT workloads using Azure DevOps.
- Automate build, testing, deployment, and release processes for Azure Databricks, Microsoft Fabric, Azure Data Factory, and Azure Data Lake solutions.
- Develop and maintain deployment strategies across development, testing, staging, and production environments.
- Build and support enterprise-scale Azure Data Lakehouse architectures.
- Work with Delta Lake and Parquet data formats to support scalable, reliable, and high-performance data processing.
- Support implementation and deployment of Medallion Architecture using Bronze, Silver, and Gold data layers.
- Configure and manage Azure Databricks workspaces, clusters, jobs, notebooks, workflows, libraries, and deployment configurations.
- Support Microsoft Fabric components including Lakehouse, Data Factory, Data Engineering, Data Warehouse, OneLake, pipelines, notebooks, and semantic models.
- Develop and maintain ETL/ELT pipelines for ingesting, transforming, processing, and publishing structured and semi-structured data.
- Work with technologies such as PySpark, Spark SQL, Python, SQL, and Databricks notebooks.
- Automate Databricks notebook, workflow, and configuration deployments using Azure DevOps pipelines.
- Implement source control and branching strategies using Git and Azure Repos.
- Establish pull request, code review, branching, tagging, and release management standards.
- Implement Infrastructure as Code using technologies such as Terraform, Bicep, ARM Templates, or Azure CLI.
- Automate provisioning and configuration of Azure data platform infrastructure.
- Manage Azure DevOps build and release pipelines using YAML-based pipeline definitions.
- Configure pipeline variables, variable groups, service connections, secrets, artifacts, approvals, and deployment gates.
- Integrate Azure DevOps with Azure resources using managed identities, service principals, and secure authentication mechanisms.
- Implement secure secrets management using Azure Key Vault.
- Support data ingestion from relational databases, APIs, files, cloud storage, and enterprise applications.
- Develop and maintain scalable pipelines using Azure Data Factory and Microsoft Fabric Data Factory.
- Monitor and troubleshoot ETL jobs, Databricks workloads, pipeline failures, data quality issues, and deployment problems.
- Implement logging, alerting, monitoring, and operational dashboards using Azure Monitor, Log Analytics, Application Insights, and related services.
- Optimize Spark workloads, cluster configurations, ETL pipelines, and data storage for performance and cost efficiency.
- Implement Delta Lake capabilities including ACID transactions, schema enforcement, schema evolution, time travel, MERGE operations, and optimized data processing.
- Implement data partitioning, compaction, file optimization, and performance tuning for large-scale Parquet and Delta datasets.
- Manage dependencies and deployment sequencing across data pipelines, notebooks, infrastructure, and downstream applications.
- Support automated unit testing, integration testing, regression testing, and data validation as part of CI/CD pipelines.
- Collaborate with Data Engineers to establish coding standards, reusable deployment templates, and DevOps best practices.
- Support environment configuration and promotion of data solutions across multiple Azure subscriptions and environments.
- Implement role-based access control, network security, private endpoints, managed identities, and enterprise security standards.
- Support disaster recovery, backup, high availability, and business continuity requirements for critical data platforms.
- Maintain technical documentation covering deployment procedures, architecture, pipeline configurations, release processes, and operational support.
- Participate in Agile/Scrum ceremonies including sprint planning, backlog refinement, daily stand-ups, sprint reviews, and retrospectives.
- Identify opportunities to improve deployment automation, reliability, observability, performance, and operational efficiency.
Qualifications
- 7+ years of experience in DevOps, Cloud Engineering, Data Engineering, ETL development, or related technical roles.
- Strong hands-on experience with Microsoft Azure and Azure DevOps.
- Strong experience building and maintaining CI/CD pipelines using Azure DevOps YAML pipelines.
- Hands-on experience with Azure Databricks.
- Strong knowledge of Apache Spark, PySpark, and Spark SQL.
- Experience with Microsoft Fabric, including Lakehouse, OneLake, Data Factory, notebooks, and data engineering workloads.
- Strong understanding of Azure Data Lake Storage Gen2 and cloud-based data lake architectures.
- Experience implementing Azure Lakehouse architecture.
- Strong hands-on experience with Delta Lake and Parquet data formats.
- Experience implementing Bronze, Silver, and Gold layers using Medallion Architecture.
- Strong experience developing and deploying ETL/ELT data pipelines.
- Experience with Azure Data Factory or Microsoft Fabric Data Factory.
- Strong SQL skills and experience working with large enterprise datasets.
- Experience with Python and PySpark for data processing and automation.
- Experience with Git-based source control, Azure Repos, pull requests, branching strategies, and code reviews.
- Experience with Infrastructure as Code tools such as Terraform, Bicep, or ARM Templates.
- Experience working with Azure Key Vault, service principals, managed identities, and RBAC.
- Strong understanding of Azure networking, security, private endpoints, and enterprise cloud security practices.
- Experience implementing automated testing and validation within data CI/CD pipelines.
- Experience with data orchestration, scheduling, dependency management, and workflow automation.
- Strong knowledge of data warehousing, data lakes, Lakehouse architectures, and modern analytics platforms.
- Understanding of dimensional modeling, data quality, metadata management, and data governance concepts.
- Experience with performance optimization for Spark workloads, Delta tables, and large-scale data pipelines.
- Experience monitoring and troubleshooting production data workloads.
- Familiarity with Azure Synapse Analytics, Power BI, Event Hubs, Functions, Logic Apps, or related Azure services is considered an asset.
- Experience with Docker, Kubernetes, or Azure Kubernetes Service is considered an asset.
- Experience with Databricks Asset Bundles, Databricks CLI, or similar automated deployment frameworks is considered an asset.
- Familiarity with Unity Catalog and enterprise data governance is considered an asset.
- Strong understanding of the Software Development Life Cycle (SDLC) and Agile delivery methodologies.
- Excellent troubleshooting, analytical, communication, and problem-solving skills.
- Ability to collaborate effectively with Data Engineers, Data Architects, Developers, DevOps Engineers, BI teams, QA teams, and business stakeholders.
- Experience working with large enterprise or government/public sector data environments is considered an asset.
Job Type: Fixed term contract
Pay: $50.00-$65.00 per hour
Benefits:
- Work from home
Experience:
- Data modeling: 7 years (required)
Work Location: Hybrid remote in Brampton, ON L6Y 2Y2
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