We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible. As a Lead Software Engineer at JPMorganChase within the Corporate Technology, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm's business objectives.

Job responsibilities:

• Implement and maintain end-to-end data governance solutions that operationalize enterprise data standards, policies, and procedures.
• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.

g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.

• 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.
• Create and maintain enterprise data models (conceptual, logical, physical) that represent business processes and support analytics.
• Define, document, and maintain metadata standards, including business glossary and data dictionary artifacts to enable consistent data understanding and usage.
• Implement and administer data cataloging capabilities and ensure data lineage tracking from source through transformations to consumption.
• Build and maintain governed ETL/ELT pipelines and patterns that align to governance requirements.
• Implement technical data quality controls, including profiling, rule definition, monitoring, and issue remediation workflows.
• Partner with cross-functional stakeholders (architecture, analytics, compliance) to ensure governance controls are adopted and sustainable. Required qualifications, capabilities, and skills:
• Expert proficiency in data engineering fundamentals: ETL/ELT development, data integration patterns, and distributed processing.
• Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery.

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