As a Software Engineer II at JPMorgan Chase within the Commercial and Investment Bank, you are part of an agile team that works to enhance, design, and deliver the software components of the firm’s state-of-the-art technology products in a secure, stable, and scalable way. As an emerging member of a software engineering team, you execute software solutions through the design, development, and technical troubleshooting of multiple components within a technical product, application, or system, while gaining the skills and experience needed to grow within your role.

Job responsibilities

• Design, develop, and maintain scalable data pipelines and ETL processes to support data integration and analytics. 
• Frequently utilizes SQL and understands NoSQL databases and their niche in the marketplace
• Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards.
• Contribute to data modernization efforts by leveraging cloud solutions and optimizing data processing workflows
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation
• Perform data extraction and implement complex data transformation logic to meet business requirements
• Ensure data availability and accuracy for analytical purposes, implement best practices for data engineering, ensuring data quality, reliability, and performance, monitor and executes data quality checks.
• Identify opportunities for process automation within data engineering workflows
• Deploy and manage containerized applications using Kubernetes (EKS) and Amazon ECS.
• Implement data orchestration and workflow automation using  AWS step , Event Bridge 
• Use Terraform for infrastructure provisioning and management, ensuring a robust and scalable data infrastructure.
  

Required qualifications, capabilities, and skills

• Formal training or certification on Data Engineering concepts and 3+ years applied experience
• Experience across the data lifecycle
• Experience working with modern Lakehouse : Databricks )
• Proficient in SQL coding (e.g., joins and aggregations) including RDBMS skills
• Experience in Micro service based component using ECS or EKS 
• Working understanding of NoSQL databases
• Experience in building and optimizing data pipelines, architectures, and data sets ( Glue or Data bricks ETL) 
• Proficient in object-oriented and object function scripting languages (Python etc.)
• Experience in developing ETL process and workflows for streaming data from heterogeneous data sources
• Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, testing, troubleshooting, or documentation) with demonstrated ability to critically evaluate and validate AI-generated outputs.
• Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations.

Preferred qualifications, capabilities, and skills

• Strong analytical and problem-solving skills, with attention to detail.
• Experience building Pipeline on AWS using Terraform and  using CI/CD pipelines 
• Knowledge of RDBMS like Aurora

• Experience with data pipeline and workflow management tools (Airflow, etc.)