Data Engineer

Be part of a dynamic team where your distinctive skills will contribute to a winning culture and team.

As a Data Engineer III at JPMorganChase within the Consumer and Community Banking, you serve as a seasoned member of an agile team to design and deliver trusted data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. You are responsible for developing, testing, and maintaining critical data pipelines and architectures across multiple technical areas within various business functions in support of the firm s business objectives.

Job responsibilities

• Supports review of controls to ensure sufficient protection of enterprise data
• Advises and makes custom configuration changes in one to two tools to generate a product at the business or customer request
• Uses enterprise-authorized AI capabilities within the work environment to accelerate data analysis support and technical documentation (e.g., clarifying requirements and drafting data definitions), validating outputs and handling data according to sensitivity and security requirements.
• Applies reuse-first, AI-assisted approaches to improve data quality checks and model/change validation routines, ensuring results are validated and aligned to resiliency and security expectations.
• Updates logical or physical data models based on new use cases
• Frequently uses SQL and understands NoSQL databases and their niche in the marketplace
• Adds to team culture of diversity, opportunity, inclusion, and respect

Required qualifications, capabilities, and skills

• Formal training or certification on data engineering concepts and 3+ years applied experience
• Hands-on experience using PySpark, AWS, and Python for creating data pipelines while also owning data architecture, data quality, scalability, governance, and operational reliability of the data platform
• Working knowledge of 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 or model change summaries) before use, escalating when uncertain and following data handling requirements.
• Advanced at SQL (e.g., joins and aggregations)
• Working understanding of NoSQL databases
• Significant experience with statistical data analysis and ability to determine appropriate tools and data patterns to perform analysis

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