Python Backend Software Engineer II - Athena / SQL

Houston, TX, United States2+ years

As a Software Engineer II at JPMorganChase within the Commodities Technology team of the Commercial & Investment Bank, you will design and build robust backend systems that power critical data workflows supporting global commodities trading operations. You will work primarily on the Athena platform, contributing to backend services and data support functions — including end-of-day Risk and Profit & Loss support — that enable traders and business stakeholders to make informed, real-time decisions. Your work will sit at the intersection of engineering excellence and business impact — helping ensure data integrity, system reliability, and scalable architecture across a high-throughput environment. This is an opportunity to grow your technical craft alongside experienced engineers while contributing to systems that matter on a global scale.

Job responsibilities

Execute standard software solution design, development, and technical troubleshooting with consideration of upstream and downstream systems and their technical implications, ensuring secure and high-quality code using at least one modern programming language
Leverage enterprise-authorized AI coding assist tools to improve code quality, delivery speed, and productivity — including code generation, refactoring, unit test creation, and documentation — while validating outputs through peer review, automated testing, and secure coding standards
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 through automation
Build and optimize data pipelines and support functions that ensure accurate, timely delivery of market and trade data to downstream consumers, including day-to-day end-of-day Risk and Profit & Loss support
Gather, analyze, and draw conclusions from large, diverse data sets to identify problems and contribute to decision-making in service of secure, stable application development
Learn and apply system processes, methodologies, and skills for the development of secure, stable code and systems across the full Software Development Life Cycle
Collaborate with front-office technology teams and business stakeholders to translate requirements into scalable, maintainable software solutions
Participate in Agile ceremonies including sprint planning, stand-ups, and retrospectives to support iterative delivery and continuous improvement
Apply technical troubleshooting to break down solutions and resolve technical problems of varying complexity across multi-layered systems
Day to Day Support for EOD Risk and PNL support.

Required qualifications, capabilities, and skills

Formal training or certification on software engineering concepts and 2+ years applied experience
Hands-on practical experience in system design, application development, testing, and operational stability
Experience developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages, with a preference for Python and Oracle/SQL
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment — such as 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 and outputs, and adherence to resiliency and security expectations
Experience across the full Software Development Life Cycle
Exposure to Agile methodologies and practices such as continuous integration and delivery, application resiliency, and security

Preferred qualifications, capabilities, and skills

Experience working with or supporting trading platforms, financial data systems, or commodities/energy technology environments, including end-of-day Risk and Profit & Loss workflows
Familiarity with cloud platforms and cloud-native development concepts (e.g., AWS, Azure, or GCP) and distributed systems principles
Hands-on experience with AI or machine learning frameworks (e.g., scikit-learn, TensorFlow, or PyTorch) applied to data engineering or analytical use cases
Experience integrating large language model-based tools or AI-powered workflows into software development or data support pipelines
Knowledge of data governance, data quality practices, or data lineage concepts in a financial services context

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