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Tier-1 employer, mid-level generalist data engineer, metro location, and broad Azure/Databricks skillset drive high competition.
Requires cloud-specific Azure and Databricks expertise, transferable across industries but needs specialized cloud data skills.
Explicit 5-9 years requirement plus mandatory Azure, Databricks and PySpark skills make shortlisting highly strict.
Design, develop, and maintain scalable data pipelines using Databricks (PySpark, Spark SQL), Azure Data Factory, and other Azure data services.
Optimize data workflows for performance, implement ETL/ELT processes, and ensure data integrity and availability for analytics and BI.
Collaborate with data scientists, analysts, and DevOps teams to deliver and deploy data solutions and maintain documentation of data architecture and workflows.
5 to 9 years of experience in data engineering focused on Azure and Databricks.
Bachelor’s or Master’s degree in Computer Science, Information Technology, or related field.
Proficiency in Databricks (including Serverless SQLWH, Unity Catalog, Lakehouse, Medallion Architecture) and Azure Data Services (Azure Data Factory, Data Lake Storage, SQL Database, Key Vault).
Strong expertise in writing and optimizing complex SQL queries and using PySpark for data processing; relevant certifications are a plus.
Experienced in building and optimizing enterprise-scale data pipelines on Azure cloud and Databricks platforms.
Strong technical operator comfortable with complex ETL/ELT workflows, data governance, and performance tuning.
Capable of cross-functional collaboration with data science, analytics, and DevOps teams in a fast-paced, production environment.