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Tier-1 brand, mid-level generalist data role in metro locations drives high candidate competition.
Core data engineering skills are transferable, though banking controls and AI-validation increase domain specificity.
Explicit 3+ years, certifications, advanced SQL, and security/AI validation requirements increase filtering strictness.
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Design, develop, test, and maintain critical data pipelines and architectures supporting multiple business functions.
Leverage enterprise-authorized AI tools to accelerate data pipeline design, analysis, validation, and documentation with adherence to data sensitivity and security requirements.
Review controls to ensure enterprise data protection and update data models based on new use cases.
3+ years applied experience in data engineering with formal training or certification in data engineering concepts.
Strong advanced SQL skills and working knowledge of NoSQL databases.
Experience with statistical data analysis and selecting appropriate tools and data patterns for analysis.
Work Experience Required: 3+ years applied experience in data engineering.
Experienced in using enterprise-authorized AI capabilities to support data engineering workflows with rigorous validation and compliance to data sensitivity policies.
Proven ability to customize and configure tools to generate product outputs meeting business or customer requests.
Skilled in applying reuse-first, AI-assisted practices to strengthen software development lifecycle quality for data pipelines, including control validation and auditability.