AWS Software Engineer III-ETL/AI

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 firm’s business objectives.

Job responsibilities

• 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

Required qualifications, capabilities, and skills

• 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

Preferred qualifications, capabilities, and skills

• 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