Azure Data Engineer
14 hours ago
Toronto, Ontario, Canada
Ardent SoftSol Inc.
Full-time
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Role: Azure Data Engineer – Data Services Engineering (Transformation & Orchestration)
Location:
Toronto, Ontario Duration: Fulltime/ Contract Work Type: Hybrid: (3 days office, 2 days remote)
• Strong hands-on experience with Azure Data Factory (ADF), Azure Databricks, Python, PySpark, SQL, and Azure cloud services.
• Expertise in developing scalable data ingestion, transformation, and orchestration pipelines using Databricks and Apache Airflow.
• Experience implementing Medallion Architecture (Bronze, Silver, Gold), Delta Lake, and reusable data engineering frameworks.
• Develop reusable Databricks notebooks, data transformation components, orchestration workflows, and automated data quality checks.
• Strong experience with Azure DevOps, Git, CI/CD pipelines, and automated deployment processes.
• Experience with Spark performance tuning, pipeline troubleshooting, data reconciliation, and workload optimization.
• Knowledge of data governance, metadata management, Unity Catalog, and enterprise data engineering best practices.
• For Lead Engineers: Experience in solution design, technical architecture, code reviews, mentoring, and collaboration with client architects.
• Good to have: Databricks Asset Bundles, REST APIs, and integration with enterprise data platforms.
Location:
Toronto, Ontario Duration: Fulltime/ Contract Work Type: Hybrid: (3 days office, 2 days remote)
• Strong hands-on experience with Azure Data Factory (ADF), Azure Databricks, Python, PySpark, SQL, and Azure cloud services.
• Expertise in developing scalable data ingestion, transformation, and orchestration pipelines using Databricks and Apache Airflow.
• Experience implementing Medallion Architecture (Bronze, Silver, Gold), Delta Lake, and reusable data engineering frameworks.
• Develop reusable Databricks notebooks, data transformation components, orchestration workflows, and automated data quality checks.
• Strong experience with Azure DevOps, Git, CI/CD pipelines, and automated deployment processes.
• Experience with Spark performance tuning, pipeline troubleshooting, data reconciliation, and workload optimization.
• Knowledge of data governance, metadata management, Unity Catalog, and enterprise data engineering best practices.
• For Lead Engineers: Experience in solution design, technical architecture, code reviews, mentoring, and collaboration with client architects.
• Good to have: Databricks Asset Bundles, REST APIs, and integration with enterprise data platforms.