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Metro location and common Data Engineer title increase competition, balanced by niche Databricks/Azure requirements.
Core data engineering skills transferable, but Azure Databricks specificity raises fit sensitivity to medium.
Explicit 7-10 years plus mandatory Databricks and Azure skills create high filtering.
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Design, construct, and maintain scalable data management systems using Azure Databricks to meet end-user expectations.
Lead the module through planning, estimation, implementation, monitoring, and tracking of data engineering projects.
Develop and optimize data pipelines and semantic models using PySpark, Python, SQL, and Azure data products, ensuring quality and SLA adherence.
7 - 10 years of relevant work experience in Azure data engineering or related field.
Proven expertise in Azure Data Engineering including Microsoft Fabric, Azure Synapse Analytics, and Azure Databricks.
Strong knowledge of medallion architecture and semantic model design and implementation.
Experience with DevOps practices and tools like Azure DevOps or GitHub for CI/CD pipelines.
Experienced in designing and implementing complex data architectures involving bronze, silver, and gold layers following medallion architecture.
Able to operate independently and manage end-to-end delivery under tight timelines with strong stakeholder communication.
Familiar with supporting Azure data technologies such as Power BI, Purview, Azure SQL Database, Cosmos DB, and cloud infrastructure tooling (ARM, BICEP, Terraform).