Data Engineer III - Python, Databricks

Secure, scalable data work in Houston. JPMorgan Chase is hiring a Data Engineer III (onsite in Houston, TX) to design and deliver reliable data collection, storage, access, and analytics solutions. You will build and maintain secure, stable data pipelines using Python and Databricks, with strong emphasis on data modeling, security controls, and AI-assisted development and validation.

What you’ll focus on

• Develop workflows and ELT pipelines using Python and Databricks.
• Use SQL frequently, including joins and aggregations, and apply working knowledge of NoSQL databases.
• Implement data protection aligned to enterprise requirements using entitlements frameworks.
• Update logical and physical data models as new use cases emerge, supporting consistent access and analytics.
• Contribute to review of controls to ensure sufficient protection of enterprise data.
• Use enterprise-authorized AI capabilities to accelerate pipeline and design analysis and documentation, validating outputs and handling data according to sensitivity and security requirements.
• Apply reuse-first, AI-assisted practices to strengthen SDLC routines such as test generation and control validation, with traceability/auditability and alignment to resiliency and security expectations.

Required qualifications

• Formal training or certification on software engineering concepts plus 3 years of applied experience.
• Good working knowledge of AWS, Databricks, and Python, including experience across the data lifecycle.
• Advanced ability with SQL (including joins and aggregations) and working understanding of NoSQL databases.
• Significant experience with statistical data analysis, including choosing appropriate tools and data patterns for analysis.
• Ability to utilize AWS Cloud Services to develop, deploy, and manage applications at scale.
• Daily proficiency with AI coding assistants, including tools such as Cursor, GitHub Copilot, and Claude, for code generation, documentation, and refactoring.
• Effective prompt engineering, providing context, clear goals, relevant source material, and defined output expectations to produce usable code.
• Critical evaluation and validation of AI-generated code by identifying hallucination patterns, security vulnerabilities, and logic errors prior to production deployment.
• Demonstrated experience using enterprise-authorized AI capabilities in data engineering workflows with strong validation habits and awareness of data sensitivity.
• Ability to review and validate AI-assisted outputs (examples include query suggestions, test ideas, and model change summaries) before use, escalating when uncertain and following data handling requirements.

Preferred capabilities

• Familiarity with Medallion architecture and standardized data layer practices.
• Exposure to Aurora Postgres and MongoDB.
• Experience developing and supporting AWS GLUE Jobs and a Federated Data Lake.
• Experience designing efficient data models, including normalization, denormalization, and schema design with knowledge of relational and star schemas.
• Augmented development workflow experience: integrating tools into CI/CD, using Docker, and leveraging AI to bridge language gaps (such as transitioning between Python, JavaScript, or Java).

Technology stack

Python, Databricks, ELT, SQL, NoSQL, AWS, Cursor, GitHub Copilot, Claude, Aurora Postgres, MongoDB, AWS GLUE Jobs, Federated Data Lake, Medallion architecture, Docker, CI/CD, JavaScript, Java

Benefits

• Comprehensive health care coverage
• On-site health and wellness centers
• Retirement savings plan
• Backup childcare
• Tuition reimbursement
• Mental health support
• Financial coaching