Lead Software Engineer: Platform & Journey Builder

JPMorgan Chase & Co.•New York, NY

8d•$152,000 - $215,000

About The Position

As a Lead Software Engineer at JPMorgan Chase within the Consumer & Community Banking’s Customer Platform team, you are an integral part of an agile team that works to enhance, build, and deliver trusted, market-leading identity and onboarding solutions in a secure, stable, and scalable way. You will play a key role in designing and delivering a shared identity platform that supports customer onboarding, identity verification, and trust services across multiple products and lines of business.

Requirements

Develop workflow orchestration and policy-driven journey engineering
Experience with event-driven architecture (Kafka or equivalent) event streaming platform, including publishing, consuming, and schema governance
Build Component library design - reusable, composable primitives that hold across business unit variation without forking the underlying implementation
Front-end developer experience (JavaScript, TypeScript, etc.)
CI/CD pipeline engineering and deployment automation experience
Experience developing and implementing role-based access experience - defining and maintaining service contracts and API boundaries in a multi-team environment
Develop implement Policy-as-code experience - Experience translating regulatory or compliance requirements into engineering constraints
Experience with distributed tracing and observability across multi-service journey execution
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

Nice To Haves

Prior experience leading migration from bespoke service implementations to a reusable component model in a regulated industry
Multi-tenant, multi-LOB design with RBAC enforced at the organizational boundary - not naming conventions
AI co-development toolchain ownership - tool selection, rollout governance, and compliance frameworks for AI-generated code in regulated environments
Experience in a co-development or strategic vendor partnership with active roadmap influence - not just vendor consumption
API platform and developer portal engineering at enterprise scale

Responsibilities

Build the Identity Onboarding Orchestrator the configurable, policy-aware workflow engine governing the full onboarding journey and enforcing compliance completion requirements before any journey is marked done
Design configurable journey frameworks that expose the right configuration surface to product teams without forking the underlying implementation
Design and enforce RBAC LOB as organizational namespace, role boundary definition, and access controls that protect line-of-business separation at the data model level
Build and maintain event-driven integrations event streaming patterns that decouple journey outcomes from downstream system dependencies and enable regulatory change propagation through a single update
Own observability across multi-service journey execution distributed tracing, SLO definition, and alerting that reduces mean-time-to-isolate failures from hours to minutes
Build developer facing SDKs, API documentation, sandbox environments, and integration templates that enable LOB teams to assemble and deploy compliant identity journeys without an engineering dependency
Support migration to Platformization build migration playbooks and adapter tooling that move product teams off legacy bespoke services in weeks, not quarters
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.