Senior Lead Software Engineer – Data Management

JPMorganChase•Jersey City, NJ

About The Position

As a Senior Lead Software Engineer – Data Management at JPMorgan Chase within the Commercial and Investment Bank, you will design, code, test, and deliver software and data solutions that support modernization, automation, and enterprise data strategy for customers, clients, and/or employees. You will lead data architecture, advanced analytics, and AI/ML model integration; provide technical leadership; participate in design reviews across application, platform, and data teams; and collaborate with global technical and business leaders to build software and data platforms for reliability, speed, and scale using modern technology and methodologies.

Requirements

Formal training or certification on software engineering concepts and 5+ years applied experience.
Strong hands-on experience with Java and Spring/Spring Boot, including building and consuming REST APIs.
Proficiency in ReactJS, Python and one or more additional languages such as Go or TypeScript.
Proficiency in data modeling, pipeline architecture, and enterprise storage.
Hands-on expertise developing and deploying AI/ML models to solve domain challenges.
Experience building and operating microservices and event-driven architectures.
Hands-on experience with cloud and container technologies: AWS, ECS, Kubernetes, and Docker.
Experience with CI/CD and infrastructure-as-code (Terraform).
Advanced capability in defining KPIs, building visualizations, and communicating data insights to senior stakeholders.
Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (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 senior engineers/leads on compliant usage patterns and controls.

Nice To Haves

Experience with front-end and full-stack development using React and Node.js.
Familiarity with AI/ML developer tooling (e.g., GitHub Copilot, LLMs, Claude) to enhance engineering workflows.
Experience modernizing legacy systems and driving engineering standardization (patterns, tooling, and automation).
Experience delivering enterprise business intelligence and analytical reporting to senior, globally distributed stakeholders.

Responsibilities

Lead end-to-end delivery of software features and data solutions from requirements through production release, ensuring quality and reliability.
Architect, build, and deploy high-performance microservices and full-stack applications using Java, ReactJS, Python, and REST APIs to enable seamless data access and integration across systems.
Build and maintain cloud-native services deployed on AWS (ECS, Kubernetes) using Docker and Terraform.
Design, build, and deploy AI and ML algorithms to answer critical business domain questions and deliver actionable insights.
Lead the design and production of KPIs, metrics, and analytical reporting to empower globally distributed teams to make data-backed strategic decisions.
Partner with functional leads, business stakeholders, and technology consumers to document and execute data target architectures, framework guidelines, and long-term technical roadmaps.
Improve system reliability and performance through monitoring, troubleshooting, observability, and continuous optimization (SRE practices).
Automate repetitive processes and modernize existing code paths through CI/CD pipelines to reduce operational toil and risk.
Leverage AI/ML developer tooling (e.g., GitHub Copilot, LLMs, Claude) to accelerate development and improve engineering productivity.
Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.