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Tier-1 brand, mid-level generalist data role, metro location, and broad required skillset increase competition.
Core data engineering skills transfer across industries, though banking controls and responsible-AI expectations add domain specificity.
Mandatory 3+ years plus many required technologies (Java, Spark, AWS, SQL) and controls increase shortlisting rigidity.
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Own and execute the data strategy, governance, risk management, and analytics to support business objectives.
Design, develop, test, and deploy scalable applications using Java, Python, Scala, Spark, and cloud technologies (AWS).
Integrate and apply AI-assisted software development tools responsibly within the development lifecycle to improve code quality and delivery speed.
3+ years of applied software engineering experience with formal training or certification.
Expert-level skills in Java, AWS, databases, Python, Scala, Spark, and ETL tools like Ab Initio or Informatica.
Experience with complex SQL (RDBMS), data warehousing (Star Schema), and data/analytics/big data project delivery.
Hands-on experience with cloud providers (AWS/Azure/GCP) and enterprise-authorized AI-assisted development tools, including validation of AI outputs.
Experience operating within agile teams delivering large-scale data and analytics or big data solutions in banking or financial services.
Demonstrated ability to manage end-to-end data platform engineering challenges across ingestion, storage, processing, and integration.
Comfortable embedding AI-assisted development tools into workflows with strong awareness of responsible AI use, security, and compliance in data environments.