As a Senior Manager of Software Engineering at JPMorganChase within the Corporate Sector's Reference Data Engineering team, you serve in a leadership role by providing technical coaching and advisory for multiple technical teams, as well as anticipate the needs and potential dependencies of other functions within the firm.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
- Provide overall direction, oversight, and coaching for a team of entry-level to mid-level software engineers that work on basic to moderately complex tasks
- Sets and scales operating practices for enterprise-authorized AI-assisted engineering and SDLC/TLM automation across multiple teams to improve delivery speed, quality, and operational outcomes; establishes measurable expectations (e.g., throughput, defect reduction, reliability) and ensures consistent validation, security, resiliency, and reuse of proven patterns.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to drive efficiency and support capacity unlock initiatives across teams, prioritizing reuse of existing firm technology assets.
- Executes creative software solutions through innovative system design and development. Approaches complex data infrastructure challenges with ability to think beyond conventional approaches to identify opportunities to eliminate or automate remediation of recurring issues to improve operational stability of reference data systems
- 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
- Contributes to 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 . In addition, 2 + years of experience leading technologists to manage and solve complex technical items within your domain of expertise
- Experience leading multi-team adoption of enterprise-authorized AI-assisted development and delivery tools, including defining governance/ways of working (human-in-the-loop validation, quality gates), measuring outcomes, and ensuring secure handling of sensitive inputs/outputs.
- Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, resiliency/security implications, and control expectations; ability to coach managers/leads and influence leaders on safe scaling patterns.
- 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