Software Engineer III Python Backend/ Pyspark/ Databricks
JPMorgan Chase
- Location
- Plano, TX, United States
- Work model
- On-Site
- Level
- Mid
- H-1B history
- 1,524 approvals (FY2023)
- Posted
- 23h ago
Skills
About this role
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 strong focus on Python backend development, PySpark-based data processing, and SQL-driven data transformation 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, optimizing, and maintaining production-grade data pipelines Demonstrated experience with