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Strong Tier-1 brand, metro location, and broad toolset requirements increase competition.
Data engineering skills are transferable, but banking-specific analytics and compliance increase background sensitivity.
Explicit 10+ years plus extensive mandatory data engineering tech stack implies high shortlisting strictness.
Design and govern scalable, secure data platforms supporting enterprise analytics, real-time data, and AI use cases.
Lead end-to-end data engineering strategies including data architecture, ETL processes, and integration patterns to deliver measurable business outcomes.
Provide technical leadership, mentorship, and ownership of technical assets while ensuring alignment with engineering standards and operational reliability.
10+ years of design or development experience in Analytics and Data Warehousing projects.
Bachelor’s or Master’s degree in Engineering with Computer Science or Information Technology specialization.
Proficiency in big data technologies such as Apache Spark, Scala, AWS data pipelines, Python/PySpark, Oracle SQL/PL-SQL.
Work Experience Required: 10+ years in relevant data engineering roles.
Experienced in building and optimizing end-to-end data pipelines in AWS cloud and big data ecosystems involving real-time and batch processing tools like Kafka and Apache Flink.
Demonstrates technical leadership with capability to drive engineering maturity, reusable frameworks, and cross-team collaboration in complex banking environments.
Hands-on expertise in advanced SQL performance tuning, data modeling, Teradata, Cloudera Hadoop along with exposure to AI/GenAI integrations and automation frameworks.