Lead Software Engineer - Reference Data Engineering

JPMorgan Chase; Chicago (IL); On-site; USD 140,000 - 200,000; Full time

Job summary

JPMorganChase within the Corporate Sector's Reference Data Engineering seeks a Lead Software Engineer to advance real-time data products and scalable platforms. You will architect Java/Spring Boot microservices for real-time data delivery, own features end-to-end, and collaborate in an agile team delivering secure, high-quality solutions across AWS, Databricks, and on-premises.

The role emphasizes AI-assisted engineering practices, performance optimization, and robust data pipelines, with

Qualifications

• Formal training or certification on software engineering concepts and 5+ years applied experience.
• Expert-level Java proficiency with Spring ecosystem and AWS in public cloud.
• Hands-on experience delivering system design, application development, testing, and operational stability.

Responsibilities

• Executes creative software solutions, design, development, and debugging with ability to tackle complex problems.
• Develops secure, high-quality production code and reviews peers' code.
• Owns features end-to-end: requirements, design, implementation, testing, deployment, and monitoring.

Skills

Java proficiency

Spring Boot

AWS

Microservices

Kafka/Kinesis/Spark

CI/CD

REST APIs

Distributed systems

Git/Bitbucket

AI-assisted practices

Education

Formal software engineering training

Tools

Docker

Kubernetes

Terraform

Snowflake/Databricks

MongoDB

SQL

Job description

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-o