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Tier-1 brand, metro location, popular data engineer role, mid-level experience, broad tech requirements.
Data engineering skills broadly transferable, but banking-specific governance and sensitivity raise domain specificity moderately.
Explicit 3+ years and mandatory PySpark, AWS, Python, SQL and governance skills increase filtering strictness.
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Design and deliver secure, scalable data collection, storage, access, and analytics solutions supporting firm business objectives.
Develop, test, and maintain critical data pipelines and architectures across multiple technical areas and business functions.
Support data quality, model validation routines, and control reviews to ensure data protection and resiliency.
3+ years of applied experience in data engineering with formal training or certification in data engineering concepts.
Hands-on experience with PySpark, AWS, Python for data pipelines and ownership of data architecture, quality, scalability, governance, and platform reliability.
Advanced SQL skills; working knowledge of NoSQL databases.
Experience using and validating enterprise-authorized AI capabilities in data engineering workflows with strong data sensitivity awareness.
Experienced in enterprise-scale agile environments delivering data engineering solutions aligned to business needs.
Comfortable integrating AI-assisted tools for improving data workflows while ensuring robust validation and compliance.
Skilled in both technical development and data governance to balance innovation with risk management and data security.