Software Engineer II: Data Engineer

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

As a Data Engineer II at JPMorgan Chase within the Consumer & Community Banking Connected Commerce – Banking Payments organization, you serve as a seasoned member of an agile team responsible for designing and delivering trusted data collection, storage, access, and analytics solutions in a secure, stable, and scalable manner. You develop, test, and maintain critical data pipelines and data architectures across multiple technical domains and business functions, ensuring high-quality, reliable data capabilities that support and advance the firm’s strategic business objectives.

Job responsibilities

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Supports review of controls to ensure sufficient protection of enterprise data

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Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems .

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Creates secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems.

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Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development.

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Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems.

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Proactively identifies hidden problems, patterns in data, and uses these insights to drive improvements to coding hygiene and system architecture.

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Contributes to software engineering communities of practice and events that explore new and emerging technologies

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Updates logical or physical data models based on new use cases

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Frequently uses SQL and understands NoSQL databases and their niche in the marketplace

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Leverages 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; contributes learnings and reusable patterns to improve broader team effectiveness.

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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.