Lead Software Engineer - Data Engineering & Applied AI

Job description

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible. As a Data Engineering & Applied AI Lead Software Engineer at JPMorganChase within the Asset & Wealth Management, 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

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.

• 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 Required qualifications, capabilities, and skills: 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 tool/function calling, autonomy tiers, access-controlled RAG, evaluation/monitoring, audit trails, rollback/fallback) while leading through influence, design reviews, and mentoring.

Preferred qualifications, capabilities, and skills

Preferred financial services or Wealth Management experience, data governance or data management domain expertise, and experience evaluating and enterprise-hardening open-source software.

Frontend

React or Angular with TypeScript.

Backend

Python, Java, or Kotlin with REST or GraphQL.

Data Engineering/Platform

Spark, Kafka, Airflow, Snowflake, and AWS.

Platform engineering

containers, CI/CD, observability, and controlled release practices.

Applied AI with LLMs

Claude Code, Agent, Skills, RAG, embeddings, prompt or configuration management, evaluation and guardrails, and agent tool or function calling Bachelor’s degree required. Advanced degree is a plus