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Metro location plus mid-level experience but specialized Azure Databricks skillset moderates applicant density.
Data engineering skills are transferable, but Azure Databricks specialization increases domain-specific bias.
Explicit 6–8 years requirement plus mandatory Azure Databricks, PySpark and Advanced SQL makes filtering strict.
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Lead design and implementation of secure, scalable, and highly available cloud-based solutions on Azure platform.
Take ownership of Azure Databricks and PySpark development including performance tuning and integration with data warehouses and data lakes.
Collaborate with clients to translate business requirements into technical cloud solutions involving Azure Data Factory (ADF), ADLS Gen2, and Azure Databricks.
6 to 8 years of experience in Azure data engineering focused roles.
At least 4 years of hands-on experience specifically with Azure Databricks and PySpark.
Mandatory technical skills: Azure Databricks, Python, PySpark, Advanced SQL.
Experience with integration of different data sources with Data Warehouse and Data Lake; familiar with GitHub, Jira, Teams, and Confluence.
Experienced in cloud data platform design with strong grasp of data modeling and data architecture concepts on Azure.
Capable of clearly articulating technical advantages and disadvantages of various technologies and platforms to stakeholders.
Proven ability to independently lead end-to-end Azure data solutions and provide performance tuning expertise.