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Mid-level data engineer in Bengaluru with generalist PySpark/Python requirements drives high applicant competition.
Core data engineering skills transferable across industries, though banking preference moderately increases domain specificity.
Explicit 5+ years requirement plus mandatory PySpark/Python and big-data tech stacks increase shortlisting rigidity.
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Design, develop, and maintain scalable ETL pipelines and data marts using PySpark and Python for enterprise-scale Data & Analytics initiatives.
Own end-to-end software development lifecycle activities including development, UAT support, bug fixes, production deployments, and post-production support for data engineering solutions.
Optimize PySpark code and complex SQL queries; ensure data quality, integrity, and consistency across large-scale structured, semi-structured, and unstructured datasets.
5+ years of commercial experience in Data Engineering or related roles with strong Python and PySpark expertise.
Experience in building ETL pipelines, data marts, and production-grade data engineering solutions.
Proficiency in Big Data technologies: Apache Spark (PySpark), Hadoop, Hive, and SQL/NoSQL databases.
Work Experience Required: 5+ years; Notice period: Not explicitly mentioned in the JD.
Demonstrated ability to manage end-to-end SDLC ownership for large-scale data engineering projects in Agile environments.
Hands-on expertise in debugging, optimizing PySpark and SQL code, and deploying production-grade data pipelines with CI/CD practices.
Preferably has domain experience in banking & financial services or digital products with enterprise-scale analytics platforms.