Are you ready to shape the future of data engineering at JPMorganChase? Join a dynamic team where your unique skills will help build innovative solutions and contribute to a winning culture. You'll have opportunities for career growth, collaborate with talented professionals, and make a real impact on our business objectives. Your expertise will empower our teams and drive success across the firm.
As a Software Engineer III at JPMorganChase within Investment Banking Data Products, you will design and deliver reliable data collection, storage, access, and analytics solutions that are secure, stable, and scalable. You will develop, test, and maintain essential data pipelines and architectures, supporting various business functions to achieve the firm's goals. Working alongside talented engineers, you will use your skills to drive innovation and help shape our team culture — one built on excellence, collaboration, and continuous improvement.
Job responsibilities
- Develop workflows and extract, load, and transform pipelines using Python and Databricks to support scalable and reliable data solutions
- Support the review of controls to ensure sufficient protection of enterprise data across the data lifecycle
- Implement data security using entitlements frameworks to safeguard sensitive information
- Update logical and physical data models based on evolving business use cases and requirements
- Apply SQL expertise — including complex joins and aggregations — and leverage working knowledge of NoSQL databases to support diverse data access patterns
- Apply reuse-first, AI-assisted practices to strengthen software development lifecycle quality routines for data pipelines (e.g., test generation and control validation), ensuring traceability, auditability, and alignment to resiliency and security expectations
- Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards
- 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 proficient applied experience
- Good working knowledge of cloud-based data services (especially Glue jobs and Federated Data Lake), unified analytics platforms, and Python
- Experience across the data lifecycle, including ingestion, transformation, storage, and access patterns
- Advanced proficiency in SQL, including joins and aggregations, with a working understanding of NoSQL databases
- Significant experience with statistical data analysis and the ability to determine appropriate tools and data patterns for analysis
- Experience utilizing cloud services for developing, deploying, and managing applications at scale
- Good understanding and working knowledge of software development lifecycle tools used for configuration management, continuous integration and delivery pipelines, unit testing, regression testing, and performance testing
- Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., 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/outputs, and adherence to resiliency and security expectations
Preferred qualifications, capabilities, and skills
- Familiarity with standardized data layer practices such as Medallion architecture
- Exposure to relational database platforms and cloud data warehousing solutions
- Curiosity and foundational understanding of generative AI, large language models, and AI/ML solutions
- Skills in designing efficient data models, including normalization, denormalization, and schema design, with an understanding of relational and star schemas