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Metro mid-level data role with specialized Databricks/PySpark reduces but remains moderately competitive.
Core data engineering skills transferable, but Azure/Databricks enterprise specifics increase industry sensitivity.
Explicit 4+ years plus mandatory PySpark, Databricks, and Azure skills create strict filters.
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Design, develop, and optimize Azure-based ETL/ELT data pipelines using PySpark, Spark SQL, and Databricks.
Build and orchestrate data workflows in Azure, ensuring performance, scalability, and cost efficiency.
Collaborate with cross-functional teams to define data requirements, troubleshoot pipeline issues, and support CI/CD and version control processes.
Bachelor's degree in Computer Science, Engineering, or related field.
Minimum 4 years of hands-on experience in data pipeline development and data engineering.
Proficiency in PySpark, Spark SQL, Azure services (ADF, Databricks, ADLS), and SQL performance tuning.
Experience with CI/CD processes, Git, and cloud-native data integration tools (e.g., ADF, HVR/Fivetran).
Experienced with Azure cloud-native data services and enterprise data integration between on-prem and cloud environments.
Demonstrates operational focus on pipeline performance optimization and cost management in cloud environments.
Familiar with data governance, documentation, orchestration tools, and collaborative workflows with data analysts and scientists.