About this role
You’re ready to gain the skills and experience needed to grow within your role and advance your career — and we have the perfect software engineering opportunity for you.
As a Software Engineer III at JPMorgan Chase within the Corporate Sector , you will design, build, and maintain scalable backend data engineering solutions that power critical financial reporting and analytics capabilities across the firm. You will work closely with cross-functional teams to deliver high-quality, reliable data pipelines and platform modules that support data-driven financial decision-making at enterprise scale. Your work will directly influence how the firm processes, manages, and leverages financial data to serve millions of customers and stakeholders every day.
Job responsibilities
- Execute software solutions, design, development, and technical troubleshooting with the ability to think beyond routine or conventional approaches to build solutions or break down complex technical problems
- Create secure and high-quality production code and maintain algorithms that run synchronously with appropriate systems, with a focus on Python backend development and PySpark-based data processing
- Build, deploy, and support scalable data engineering modules on Databricks, ensuring reliable performance, maintainability, and alignment with platform architecture standards
- Leverage 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; contribute learnings and reusable patterns to improve broader team effectiveness
- Apply 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
- Produce architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development
- Gather, analyze, synthesize, and develop visualizations and reporting from large, diverse financial data sets in service of continuous improvement of software applications and systems
- Proactively identify hidden problems and patterns in data and use these insights to drive improvements to coding hygiene and system architecture
- Participate in code reviews, providing and incorporating constructive feedback to continuously elevate code quality and engineering standards across the team
- Collaborate with product managers, architects, and cross-functional engineering teams to translate finance business requirements into scalable technical solutions
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 3+ years applied experience
- Hands-on practical experience in system design, application development, testing, and operational stability
- Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages
- Proficiency in Python backend development with hands-on experience building and optimizing PySpark data pipelines in production environments
- Demonstrated experience with Databricks, including module development, deployment, and ongoing operational support
- 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
- Solid understanding of databases (Snowflake, Databricks, SQL, DB2 or NOSQL)
Preferred qualifications, capabilities, and skills
- Experience with Apache Spark, Kafka, or similar distributed data processing frameworks in a large-scale enterprise environment
- Exposure to cloud-native development and AWS services, particularly AWS EMR, Glue, ECS, EKS (Elastic Kubernetes Service) for deploying and managing containerized applications
- Exposure to AI/ML data platforms