Senior Lead Software Engineer - Data Management

Job summary

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.

Job 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.

Required qualifications, capabilities, and skills


* 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 A...