Lead Software Engineer - Data Engineering & Applied AI

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As a Data Engineering & Applied AI Lead Software Engineer at JPMorgan

Chase within the Asset & Wealth Management, youare 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.

• Designing, developing, and delivering AI-enabled platform capabilities that power next-generation data management products (Data Quality, Metadata Management, Lineage, Data Retention and Destruction)
• 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.
• Hands on design and development of agentic production data platform with full stack application ownership.
• AI first mindset in developing code using Claude Code, building agents to make application/platform autonomous.
• Develop AI skills, agents, MCP server in support of product capabilities to deliver business value
• Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
• 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.
• 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.
• Develops secure high-quality production code, and reviews and debugs code written by others
• Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems

• Formal training or certification in Software Engineering and 5+ years applied experience
• 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 practices
• Strong knowledge of data architecture and modeling patterns, including dimensional modeling and database design (normalization/denormalization).
• Use enterprise-authorized AI-assisted development tools (coding, testing, troubleshooting, documentation) with rigorous validation of outputs for correctness, performance, and security.
• Apply and coach responsible AI practices, including data sensitivity, secure input/output handling, and resiliency/security standards.
• Own end-to-end delivery of data management products: operate/maintain/modernize existing applications and build greenfield capabilities across UI, APIs, services, integrations, and data pipelines in a federated model.
• Design and deliver platform services for metadata, lineage, data contracts, data quality, and retention/destruction via well-defined interfaces and workflows.
• Evaluate open-source solutions through rapid POCs with success criteria; lead selection, customization, and enterprise hardening for reliability, security, and operational support.
• Build workflow orchestration and policy enforcement services, plus scalable batch/streaming integrations (schema evolution, backfills, error handling, contract-driven interoperability).
• Establish production-grade operations and controls (SRE practices, monitoring/on-call, incident response/RCA, auditability, least-privilege, disciplined change management) and deliver governed agentic capabilities (safe…