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Tier-1 brand, metro locations, mid-level generalist data role with popular tooling increases competition.
Core data engineering skills (Databricks, PySpark, AWS) are broadly transferable across industries.
Explicit 2+ years plus mandatory Databricks/PySpark/AWS and regulated banking controls make filtering strict.
Design, develop, and troubleshoot scalable data collection, storage, access, and analytics solutions within an agile data engineering team.
Organize and maintain data to make it actionable, applying secure access controls and AI-assisted techniques to improve data quality and validation.
Make custom configurations in data tools and update data models based on new use cases with minimal supervision.
2+ years applied experience in data engineering with formal training or certification in related concepts.
Experience with data lifecycle management, building data frameworks, data lakes, batch and real-time data processing using Spark or Flink.
Proficiency in Databricks, Python/Java, PySpark, relational and NoSQL databases, ETL pipelines, and data warehousing.
Working knowledge of AWS Glue, AWS EMR, containerized service deployment (Spring Boot/Flask on AWS EKS or Kubernetes), and enterprise-authorized AI tools usage with data sensitivity awareness.
Experienced in building and operating secure, scalable data engineering solutions in cloud-native environments (AWS, Kubernetes, Docker).
Skilled at leveraging AI-assisted workflows for data validation while ensuring data security and compliance.
Able to work autonomously on data model updates and custom configurations, contributing technical expertise and operational rigor to the team.