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Strong Tier-1 brand, popular mid-level data engineer title, and metro/mid experience amplify competition.
Core data engineering skills (Spark, Databricks, ETL) transfer easily across industries.
Explicit 2+ years, mandatory Databricks/PySpark/AWS skills and regulated bank environment raise strictness.
Design, develop, and maintain secure, scalable data collection, storage, and analytics solutions within the Commercial & Investment Bank.
Execute data engineering tasks including batch and real-time data processing using technologies like Databricks, Spark, PySpark, and AWS Glue/EMR.
Perform data quality checks, validate AI-assisted outputs, and update data models based on new use cases with minimal supervision.
2+ years of applied experience and formal training or certification in data engineering concepts.
Experience with data lifecycle management including building data frameworks, working with data lakes, batch and real-time data pipelines.
Working knowledge of Databricks, Python/Java, PySpark, AWS Glue, EMR, and deploying services on AWS EKS or Kubernetes.
Experience with relational and NoSQL databases and basic knowledge of data system components for secure data access.
Experience working in agile teams within financial services or similar regulated environments focused on secure, scalable data solutions.
Hands-on expertise in cloud-native data engineering using AWS services, containerization (Docker, Kubernetes), and big data technologies (Spark, Kafka).
Comfortable validating and integrating AI-assisted data engineering tools with strong awareness of data sensitivity and security requirements.