Data Engineer II - Databricks, Python, PySpark, AWS
JPMorgan Chase & Co. (company site, opens in a new tab)2+ yrsHyderabadOn-siteDirect career pageEst. 12–20 LPA
Posted on 2 Sept
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Job description
Structured overview of role & requirements
About This Role
- 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.
Minimum Requirements
- 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.
Ideal Candidate Profile
- 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.