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Metro location, common early-mid data skillset, and popular data-engineer title increase applicant competition.
Specific Azure/Databricks and PySpark expertise creates moderate industry transferability.
Explicit 2+ years and mandatory PySpark, Spark SQL, Azure Databricks, and ADF skills.
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Design, develop, and maintain Azure-based data pipelines using PySpark and Spark SQL.
Build and orchestrate data workflows integrating on-premise and cloud systems with tools like ADF and HVR/Fivetran.
Optimize Spark jobs for performance, scalability, and cost efficiency while supporting CI/CD and automation.
Bachelor's degree in Computer Science, Engineering, or related field.
Minimum 2 years of hands-on data engineering experience, specifically with PySpark and Spark SQL.
Proficiency in Azure services including ADF, Databricks, and ADLS.
Experience with SQL, data modeling, Git, and CI/CD pipelines.
Operationally strong in building and optimizing data pipelines in Azure cloud environments.
Experienced in integrating hybrid data systems (on-premise and cloud) and managing distributed data processing.
Comfortable working collaboratively with cross-functional teams including data analysts and scientists to meet data requirements.