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Databricks/PySpark specialization, metro location, and generalist data role create moderate competition.
Core Databricks, PySpark and ETL skills are broadly transferable across industries.
Specific mandatory tech stack (Databricks, PySpark, advanced SQL) implies moderate technical filtering.
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Design, develop, and maintain scalable data pipelines using Databricks and PySpark for batch and near real-time processing.
Optimize and ensure data quality, integrity, and cost-efficiency of ETL/ELT workflows on large distributed datasets.
Collaborate with cross-functional teams to deliver data solutions and troubleshoot pipeline failures with monitoring and alerting.
Strong hands-on experience with Databricks, PySpark/Apache Spark, and advanced SQL (joins, window functions, tuning).
Solid understanding of ETL, data warehousing, data modeling, and building scalable pipelines for structured and semi-structured data.
Familiarity with data lake / delta lake architectures and performance optimization techniques in Spark and SQL.
Work Experience Required: Not explicitly mentioned in the JD. Location: Chennai or other specified locations.
Experienced in building and optimizing Spark-based ETL pipelines in a data lakehouse environment using Databricks.
Comfortable working with large-scale distributed data and collaborating closely with Data Ops, Business, and Analytics teams.
Familiar with version control, deployment practices, and has exposure to cloud platforms (preferably AWS) and Agile/Scrum environments.