Lead Software Engineer, Reference Data Engineering

JPMorgan Chase

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Quick summary

Work type: On-site
Location: Chicago, IL
Posted: 2 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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How this pay compares to similar roles

Similar $182k

$145k most similar roles pay here $218k

This listing doesn't post a salary. Most similar roles pay $152,150–$211,200.

Based on 240 similar postings.

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About JPMorgan Chase

JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.

JPMorgan Chase currently has 1138 open roles on FindRole.

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At a glance

TL;DR · Lead Software Engineer, Reference Data Engineering

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Lead Software Engineer - Reference Data Engineering joins the Corporate Sector's Reference Data Engineering team to build, enhance, and deliver secure, scalable technology products. This role involves designing and developing Java and Spring Boot microservices for real-time data product delivery while managing end-to-end feature ownership from design through monitoring. The engineer will solve complex data infrastructure challenges by building intelligent, self-healing platforms within a modern data mesh architecture. Key responsibilities include implementing AI-assisted engineering practices, optimizing data pipelines, and ensuring high-quality production code. Technical requirements include expert-level Java proficiency, Spring Boot, Spring Cloud, and Spring Security, alongside experience with AWS services like EC2, S3, Lambda, and Kinesis. The role also requires expertise in Kafka, Spark Structured Streaming, Databricks, Snowflake, MongoDB, and SQL, while utilizing tools such as Docker, Kubernetes, Terraform, and Git to manage high-performance, event-driven data systems.

Skills

Java Spring Boot AWS Databricks Kafka Spark Structured Streaming Microservices Python Snowflake Kubernetes Terraform Docker SQL NoSQL MongoDB RabbitMQ CI/CD Git Maven REST APIs

What you'll do

Design and develop Java/Spring Boot microservices for real-time data product delivery and platform capabilities.
Own assigned features end-to-end, including requirements, design, implementation, testing, deployment, and monitoring.
Develop secure, high-quality production code while reviewing and debugging code written by other team members.
Drive the adoption of enterprise-authorized AI-assisted engineering practices to improve code quality and delivery speed.
Optimize performance, scalability, and cost efficiency for microservices and data pipelines.
Implement event-driven streaming solutions using technologies like Kafka, Kinesis, and Spark Structured Streaming.
Manage complex data infrastructure challenges across AWS, Databricks, and on-premises environments.
Proactively identify and remediate technical debt while providing production support and incident response.

What we're looking for

Formal training or certification on software engineering concepts and 5+ years of applied experience.
Expert-level Java proficiency including Spring Boot, Spring Cloud, Spring Data, and Spring Security (JWT/OAuth2).
Experience building cloud-native applications using AWS services such as EC2, S3, Lambda, CloudWatch, IAM, and Kinesis.
Proficiency in J2EE, REST APIs, and event-driven architectures using Kafka, RabbitMQ, or Spark Structured Streaming.
Hands-on experience with NoSQL (MongoDB), columnar/analytics (Snowflake, Databricks), and relational SQL databases.
Experience with version control (Git/Bitbucket), CI/CD pipelines, and DevOps tools like Docker, Kubernetes, and Terraform.
Ability to drive team adoption of enterprise-authorized AI-assisted engineering practices and automation within the SDLC.
Python experience, Databricks exposure, and AWS Certifications are preferred.

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