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Tier-1 brand, mid-level data engineer role with common skills and broad tech requirements increases competition.
Core data engineering skills are broadly transferable across industries despite some banking compliance context.
Explicit 4+ years requirement plus extensive mandatory data platform, cloud, SQL/ETL, and GenAI skills raises strictness.
Lead moderately complex technical initiatives including design, coding, testing, debugging, and documentation within data engineering domains.
Contribute to large scale planning, resolve technical challenges, and provide guidance to less experienced staff while collaborating across teams.
Own design, requirements analysis, optimization, best practices definition, and code quality improvements for modernizing enterprise data platforms.
4+ years of software engineering experience (or equivalent via work experience, training, military, education).
Hands-on experience with Python, Spark, Iceberg, Hive; SQL development and tuning skills mandatory.
Experience with ETL tools (Ab Initio preferred), cloud platforms (Azure or GCP), databases (Oracle, MS SQL, Teradata).
Work Experience Required: Minimum 4 years software engineering; Notice Period: Not explicitly mentioned.
Experienced in supporting and optimizing large enterprise-scale data environments with modern Data Warehousing, Data Lakes, and Lakehouse architectures.
Skilled in implementing cloud-native, automated, CI/CD, and DevOps best practices with open-table formats like Iceberg.
Familiarity with GenAI, Agentic AI, LLM adoption including RAG architectures, vector stores, and AI-driven data engineering enhancements.