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Tier-1 employer and Bangalore metro with mid-senior data engineering role increase applicant competition.
Specialized data engineering skills with banking context make cross-industry fit moderately sensitive.
Multiple mandatory years plus specific Big Data, cloud, and language requirements enforce strict shortlisting.
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Lead and mentor a team of entry-level to mid-level software engineers working on data engineering tasks and ETL transformations on Big Data platforms like AWS and Data Bricks.
Own architecture design and development, including acquisition, transformation, and quality assurance of complex data sets, ensuring collaboration with product and business partners.
Define and scale operating practices for AI-assisted software development lifecycle automation across multiple teams to improve delivery speed, quality, and operational outcomes.
5+ years of applied software engineering experience with 2+ years in a leadership role managing technologists in complex technical domains.
Advanced knowledge of application, data, and infrastructure architecture; experience with Big Data technologies (AWS, Spark, Kafka, Data Bricks).
Proficient in Java, Python, SQL, UNIX shell scripting, and experience implementing complex ETL transformations on Big Data platforms.
Experience leading multi-team adoption of enterprise AI-assisted development tools, understanding responsible AI use (data sensitivity, security, resiliency).
Experienced in managing cross-functional teams delivering complex data engineering solutions within agile environments.
Strong familiarity with enterprise AI automation in software delivery, including governance, quality gates, and secure handling of sensitive data.
Technical depth in Big Data ecosystems with proficiency in Java, Python, SQL, and Unix scripting, combined with ability to translate complex data requirements into actionable architecture.