AWS Software Engineer III-ETLAI

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.

As an AWS Software Engineer III-ETL/AI at JPMorgan Chase within the Commercial and Investment Banking - Market Data Lake Core Data Platform team you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure stable and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firms business objectives.

• Executes software solutions design development and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems
• Creates secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems
• Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality delivery speed and productivity across complex deliverables (e.g. code generation/refactoring unit test creation documentation) while validating outputs through peer review automated testing and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
• 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.
• Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development
• Gathers analyzes synthesizes and develops visualizations and reporting from large diverse data sets in service of continuous improvement of software applications and systems
• Proactively identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture
• Ensure optimal application performance in production providing ongoing monitoring troubleshooting and end-to-end production support to maintain stability and service reliability

• Formal training or certification on software engineering concepted and 3 years applied experience
• Hands-on practical experience in system design application development testing and operational stability
• Hands-on experience in Python or Java programming languages
• Prior experience building and operating data platforms and services using AWS including services such as S3 Redshift SageMaker and Lambda.
• Experience in developing debugging and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages
• Experience with databases and data access patterns including platforms such as Redshift and Oracle and strong SQL/data querying capability.
• Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g. for coding test creation troubleshooting or documentation) with demonstrated ability to critically evaluate validate and refine AI-generated outputs for correctness performance and security
• Understanding of responsible AI use in engineering workflows including data sensitivity considerations secure handling of inputs/outputs and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices
• Overall knowledge of the Software Development Life Cycle
• Solid understanding of agile methodologies such as CI/CD Application Resiliency and Security
• Practical experience using infrastructure-as-code tooling (e.g. Terraform) and working effectively in a large corporate engineering environment

• Big Data / distributed computing experience (best to have) including frameworks such as Apache Spark (and/or Hadoop ecosystem) for large-scale data processing
• Experience with data orchestration and workflow scheduling tools (best to have) such as Airflow or similar
• Exposure to messaging and event-driven architectures (best to have) including technologies such as Kafka and/or MQ (e.g. IBM MQ or similar)
• Experience with containerization and orchestration platforms (best to have) including Docker and Kubernetes and deploying/operating services in containerized environments
• Familiarity with Agentic AI frameworks and patterns (e.g. LangChain AutoGen CrewAI or similar) and experience applying them responsibly in production contexts
• Experience with reinforcement learning prompt engineering or agent-based simulation especially where it improves agent reliability and business outcomes
• Exposure to modern front-end technologies and patterns for integrating AI/data capabilities into user-facing experiences