Backend Python/Databricks Software Engineer III

Join one of the world's most innovative financial institutions and be part of a team that is transforming how financial data is engineered, processed, and delivered at scale. At JPMorganChase, we empower our engineers with cutting-edge tools, a collaborative culture, and the opportunity to grow your career while solving some of the most complex data challenges in financial services.
As a Software Engineer III at JPMorganChase within the Consumer & Community Banking Finance Data Engineering team in Plano, TX, 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.
Our team is built on a foundation of engineering excellence, continuous learning, and collaborative innovation. You will thrive in a fully onsite environment in Plano, TX, where your ideas are valued, your growth is supported, and your contributions make a measurable difference across the organization.
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
Overall knowledge of the Software Development Life Cycle