Are you ready to shape the future of data engineering at JPMorgan Chase? 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 Data Engineer at JPMorgan Chase within our agile team, 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 with us, you will use your skills to drive innovation and help shape our team culture. Together, we focus on excellence, collaboration, and continuous improvement.

Job responsibilities

• Develop workflows and ELT pipelines using Python and Databricks.
• Uses enterprise-authorized AI capabilities within the work environment to accelerate data pipeline/design analysis and documentation, validating outputs and handling data according to sensitivity and security requirements.
• Support review of controls to ensure sufficient protection of enterprise data.
• Implement data security using entitlements frameworks.
• Update logical or physical data models based on new use cases.
• Use SQL frequently and understand NoSQL databases
• Applies reuse-first, AI-assisted practices to strengthen SDLC-quality routines for data pipelines (e.g., test generation and control validation), ensuring traceability/auditability and alignment to resiliency and security expectations.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 3 years applied experience.
• Good working knowledge of AWS, Databricks, and Python.
• Experience across the data lifecycle.
• Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support data engineering workflows with strong validation habits and awareness of data sensitivity.
• Ability to review and validate AI-assisted outputs (e.g., query suggestions, test ideas, or model change summaries) before use, escalating when uncertain and following data handling requirements.
• Advanced at SQL, including joins and aggregations.
• Working understanding of NoSQL databases.
• Significant experience with statistical data analysis and ability to determine appropriate tools and data patterns for analysis.
• Utilize AWS Cloud Services for developing, deploying, and managing applications at scale.
• Good understanding and working knowledge of software development lifecycle tools used for configuration management, CI/CD pipelines, unit testing, regression testing, and performance testing.

Preferred qualifications, capabilities, and skills

• Familiarity with the Standardized data layer practices (Medallion architecture)
• Exposure to Aurora Postgres and MongoDB
• Skills in designing efficient data models including normalization, denormalization, and schema design and an understanding around relational and star schemas.