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Tier-1 brand, common data-engineer title, mid-level experience, and broad tech stack increase candidate competition.
Data engineering skills are transferable, but Informatica and banking compliance increase domain specificity.
Mandatory Informatica, Python, Oracle, Spark, UNIX, and compliance requirements make filtering stringent.
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Lead and manage moderately complex data engineering initiatives focused on enterprise-scale data processing frameworks and data integration.
Design, build, and maintain scalable, optimized ETL/ELT data pipelines and data warehouses supporting reporting, analysis, and quality standards.
Oversee issue resolution, production support, performance tuning, and adhere to data governance, compliance, and operational standards using AI-assisted tools where applicable.
Minimum 4 years of Data Engineering experience and 4 years of IT experience with strong expertise in data integration and ETL development.
Hands-on experience with Informatica PowerCenter/IICS, Python, UNIX/Linux, Shell scripting, SQL with query optimization, and Oracle databases including performance tuning.
Experience using Generative AI tools (e.g., GitHub Copilot, Devin AI) for development and working with Very Large Databases (VLDBs).
Knowledge of data quality, metadata management, data lineage, and enterprise job scheduling tools like AutoSys or equivalents.
Experienced in handling enterprise-level, moderately complex ETL/ELT data pipeline projects with demonstrated end-to-end ownership including design, development, and production support.
Proficient in integrating modern AI-assisted development tools to enhance coding productivity and maintainability.
Comfortable working in Agile environments with cross-functional teams and exposure to cloud platforms such as Google Cloud Platform (GCP) and modern NoSQL databases like MongoDB.