Software Engineer III - Data Engineering | Infrastructure Platforms

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level. As a Software Engineer III at JPMorganChase within the Infrastructure Platforms organization, 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. As a Software Engineer III, you contribute to the design, development, testing, deployment, and ongoing improvement of secure, production-ready applications that combine Java-based software engineering with Python-based AI/ML capabilities. The role works across the delivery lifecycle to solve business problems, improve customer experiences, and increase operational efficiency in cloud and hybrid data environments. You use your elevated technical skills to support the design and development of metrics reporting and dashboards to enable Operations Management to execute their strategic objectives and ensure conformance with all controls, policies, and procedures. Job Responsibilities Translate stakeholder and business requirements into well-defined technical solutions, prototypes, and delivery plans. Using Java and Python, create secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems Use SQL to analyze large, heterogeneous datasets, validate assumptions, generate insights, and measure solution impact. Contribute to scalable, resilient architectures and support reliable deployment, monitoring, validation, troubleshooting, and iterative improvement. Automate operational processes and help eliminate recurring issues to improve platform stability and efficiency. Create unit and integration tests and participate in code reviews and continuous validation practices. Collaborate with product owners, operations, marketing, engineering, data, and other cross-functional teams to deliver end-to-end solutions aligned with enterprise standards. Produce clear technical documentation, reports, and presentations for technical and non-technical audiences. Support model and data governance by documenting solution design and providing technical input to risk, privacy, and controls assessments 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. 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 (SDLC), testing (unit and integration), and operational stability for building enterprise cloud native applications. Understanding of software engineering fundamentals, APIs, source control, testing, debugging, and continuous integration and delivery practices. Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages (Java & Python) and database querying languages (SQL) Experience with frameworks like Spring boot, Hibernate, Envers, etc. Experience with data-intensive and statistical applications such as Jupiter notebooks Data engineering experience working with BigData, Data Mesh, Data Lake architecture, relational/graph databases, PL/SQL, data analytics and dashboards (Tableau, Qlik-sense,etc.) Working with Telemetry systems using tools like Splunk, Elastic Search, etc. Experience and solid understanding of Retrieval-Augmented Generation (RAG), agentic workflows, prompt engineering, model development and testing. 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. Exposure to cloud or hybrid platforms such as AWS and modern data or container platforms such as Databricks or Kubernetes. Preferred qualifications, capabilities, and skills Bachelor’s degree in Computer Science, with specialization Experience with Data Analytics Experience with Oracle, MongoDB etc. Experience with deploying a vector database on services like Google Kubernetes Engine (GKE), Amazon Elastic Kubernetes Service (EKS)