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Moderate—metro location and generic senior engineering title increase applicants, but Databricks/PySpark specialization limits pool.
Low—core Databricks, PySpark, and SQL skills are highly transferable across industries.
Medium—mandatory Databricks, PySpark and SQL skills but no explicit years requirement.
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Design, develop, and maintain scalable data pipelines using Databricks and PySpark for batch and near real-time data processing.
Write efficient SQL queries for data transformation, validation, and analysis on large distributed datasets.
Ensure data quality, pipeline performance optimization, and collaborate with cross-functional teams to deliver data solutions.
Strong hands-on experience with Databricks platform and PySpark/Apache Spark.
Advanced SQL skills including joins, window functions, and performance tuning.
Experience building scalable data pipelines with knowledge of data warehousing, ETL concepts, and data lake/lakehouse architectures.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in optimizing Spark and SQL for performance and cost within data lake/lakehouse environments.
Comfortable working with structured and semi-structured data pipelines and performing proactive monitoring and troubleshooting.
Familiarity with version control (Git), deployment practices, and comfortable collaborating in cross-functional teams.