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Popular data-engineer title increases applicants but Azure/Databricks specialization limits pool.
Azure Databricks and ADF specialization moderately biases backgrounds, though core data engineering skills remain transferable.
Multiple mandatory Azure Databricks, ADF, Spark, PySpark, and SQL requirements enforce strict technical filtering.
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Design, develop, and optimize scalable ETL/ELT pipelines and data processing solutions using Azure Data Factory, Azure Databricks, and Apache Spark.
Build and maintain data transformation workflows with PySpark, Spark SQL, and SQL while ensuring data quality, monitoring, and error handling.
Collaborate with stakeholders to deliver data solutions and implement CI/CD pipelines, ensuring data security, governance, and compliance on Azure platforms.
Strong hands-on experience with Azure Databricks and Azure Data Factory.
Expertise in Apache Spark, PySpark, Spark SQL, and strong SQL development skills.
Experience with Python for data engineering and knowledge of Azure Data Lake Storage, Azure SQL Database, and Azure Synapse Analytics.
Bachelor's degree in Computer Science, IT, Engineering or equivalent experience; Work Experience Required: Not explicitly mentioned in the JD.
Experienced in designing and managing large-scale data engineering workflows on Microsoft Azure with focus on performance tuning and cost optimization.
Skilled in integrating multiple Azure services into robust data platforms and implementing automated CI/CD pipelines for data engineering.
Comfortable working closely with data architects and business stakeholders to align data solutions with organizational standards for security and governance.