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 JPMorganChase 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 AWS
- Strong understanding of distributed systems, microservices architecture, and design patterns
- Production-level experience with AWS: EC2, S3, Lambda, CloudWatch, IAM, Kinesis. Hands-on with databases: NoSQL (MongoDB), columnar/analytics (Snowflake, Databricks), relational SQL
- Experience with version control (Git/Bitbucket), CI/CD pipelines, and modern DevOps practices (Docker, Kubernetes, Terraform) so you can be proficient in all aspects of the Software Development Life Cycle (SDLC), agile methodologies, and continuous delivery
- Proficiency in Java/J2EE and REST APIs. Experience building event-driven Microservices and Kafka (Kinesis, Spark Structured Streaming) and its event-driven architecture and message brokers (Kafka, RabbitMQ)
- Hands-on experience with system design, application development, testing with proficiency in GIT/Bitbucket, JIRA, Maven
- Ability to tackle design and functionality problems independently with little to no oversight. Clear articulation of technical concepts in a self-motivated role that requires high ownership of work quality and delivery
Preferred qualifications, capabilities, and skills
- Python with data engineering experience including exposure to Databricks and a a background in data infrastructure or reference data platforms
- Experience in Platform or Product Development and contribution to open-source projects or public technical content
- AWS Certifications (AWS Certified Solutions Architect - Associate or higher)
- Experience with Spring Cloud (Netflix OSS stack: Eureka, Zuul, Hystrix)
- Experience building or maintaining high-scale, real-time data systems. Familiarity with data product delivery and data mesh patterns
- Experience in multi-region or disaster recovery scenarios
- Familiarity with managed service architecture patterns