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Tier-1 employer plus a common Data Engineer title and mid-level profile increases competition.
Core PySpark/Azure data-engineering skills transfer well, but financial compliance domain increases sensitivity moderately.
Requires specific PySpark/Azure Databricks and governance skills but lacks explicit years, so medium strictness.
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Develop, optimize, and maintain ETL data pipelines using PySpark and Spark SQL within Azure Databricks environment.
Process large-scale financial data from multiple sources such as core banking, payments, and trading platforms ensuring data quality, governance, and lineage.
Collaborate with business and compliance teams, utilize error handling, logging, and monitoring to ensure performance and regulatory compliance of data pipelines.
Hands-on experience with PySpark, Spark SQL, and Azure Databricks for data pipeline development.
Experience processing large-scale financial data integrating core banking, payments, or trading systems.
Bachelor of Engineering degree.
Location: Visakhapatnam.
Experienced in performance tuning and cost optimization of Spark jobs in cloud environments.
Proficient in Unix scripting and scheduling tools supporting automated ETL workflows.
Familiar with data governance, audit processes, and regulatory compliance in financial data contexts.