Lead Software Engineer - Reference Data Engineering

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As a Lead Software Engineer at JPMorgan

Chase within the Corporate Sector's Reference Data Engineering, 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. This group powers one of the teams' most critical data-driven capabilities through a modern data mesh architecture delivering curated, domain-driven data products in real-time across the firm.

Operating at enterprise scale across AWS, Databricks, and on-premises, Reference Data Integration (RDI) manages multi-tenant data delivery with managed-service enablement. The platform uses event-driven streaming (Kafka, Kinesis, Spark Structured Streaming) for high-performance real-time data delivery and is evolving AI/ML-driven data quality and reconciliation capabilities. Join our engineering team to architect intelligent, self-healing data platforms driving the next generation of financial infrastructure

Job responsibilities

Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems

Develops secure and high-quality production code, and reviews and debugs code written by others

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.

Executes creative software solutions through innovative system design and development. Approaches complex data infrastructure challenges with ability to think beyond conventional approaches

Develops secure, high-quality production code. Reviews and debugs code written by others. Maintains and elevates engineering standards

Designs and develops Java/Spring Boot microservices for real-time data product delivery and platform capabilities

Owns assigned features end-to-end: requirements, design, implementation, testing, deployment, and monitoring

Optimizes performance, scalability, and cost efficiency of microservices and data pipelines while writing comprehensive tests (unit, integration, end-to-end) to ensure platform reliability and data integrity

Participates in design and code reviews. Proactively identifies and remediates technical debt and helps with production support and incident response. Contributes to team knowledge sharing: documentation, runbooks, tech talks

Required qualifications, capabilities, and skills

Formal training or certification on software engineering concepts and 5+ years applied experience

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 practical experience delivering system design, application development, testing, and operational stability

Expert-level Java proficiency. Deep knowledge of Spring Boot, Spring Cloud, Spring Data, and Spring Security (JWT/OAuth2), Spring Framework, Spring Boot, and AWS Services in public cloud infrastructure, with experience building cloud-native or cloud-ready applications using AWSStrong understanding of distributed systems, microservices architecture, and design patterns

Production-level experience with AWS: EC2, S3, Lambda, Cloud Watch, IAM, Kinesis. Hands-on with databases:
No

SQL (MongoDB), columnar/analytics (Snowflake, Databricks),…