Data Engineer II - Databricks, Python, PySpark, AWS
JPMorgan Chase & Co. (company site, opens in a new tab)2+ yrsHyderabad, BengaluruOn-siteDirect career pageEst. 12–22 LPA
Posted on 2 Sept
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Job description
Structured overview of role & requirements
About This Role
- 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.
Minimum Requirements
- 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.
Ideal Candidate Profile
- 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.