Principal Java Software Engineer - Executive Director of Software Engineering

JPMorgan Chase & Co.•Plano, TX

15h

About The Position

As a Director of Software Engineering at JPMorganChase within the Asset & Wealth Management, Equity Plan Services Platform Team, you lead multiple technical areas, manage the activities of multiple departments, and collaborate across technical domains. Your expertise is applied cross-functionally to drive the adoption and implementation of technical methods within various teams and aid the firm in remaining at the forefront of industry trends, best practices, and technological advances. We are looking for a deeply technical, hands-on Director of Software Engineering to lead and work shoulder-to-shoulder with our engineering team on the Equity Plan Services platform — a high-throughput, regulated financial system handling trade execution, RSU/ESPP release processing, cash movement orchestration, and client reporting at scale. You will write code, design distributed systems, own production reliability, and drive architecture decisions across the stack — from Spring Boot microservices and Kafka event pipelines to AWS-native infrastructure and SQL/NoSQL data layers. Equally important is your ability to communicate with precision: working directly with Business, Trading, and Operations teams to translate requirements into engineering solutions and explain technical trade-offs without losing the room.

Requirements

Formal training or certification on software engineering concepts and 10+ years applied experience.
In addition, 5+ years of experience leading technologists to manage, anticipate and solve complex technical items within your domain of expertise
Experience developing or leading large or cross-functional teams of technologists
Expert-level backend engineering in Java (Spring Boot, Spring Integration, Hibernate/JPA) and/or Python; strong SQL and data modeling across relational and NoSQL systems.
Deep AWS experience: ECS/EKS, Lambda, Aurora, DynamoDB, S3, Glue, CloudWatch — able to design, deploy, and operate cloud-native systems end-to-end.
Hands-on Apache Kafka experience: topic design, consumer group management, exactly-once delivery, and stream processing patterns.
Solid grasp of distributed systems fundamentals: consistency models, CAP trade-offs, idempotency, distributed transactions, and failure modes.
Strong CI/CD and test engineering practice: you build the pipelines and write tests alongside the team.
Excellent communicator — able to move fluidly between engineering teams and business/operations stakeholders in the same conversation.
Experience leading multi-organization adoption of agentic AI-enabled engineering operating models (using enterprise-authorized tools within the work environment), including defining governance (human-in-the-loop decisioning, quality gates), measurement frameworks, and secure handling of sensitive inputs/outputs across teams.
Deep understanding of responsible AI risk, controls, and resiliency/security expectations at scale, with demonstrated ability to advise senior leaders on safe adoption, portfolio governance, and reuse-first strategies.

Nice To Haves

Proven experience delivering in a regulated environment (financial services preferred) with working knowledge of audit, entitlement, and compliance requirements.

Responsibilities

Directly manages multiple engineering domains with a strategic focus on microservices and cloud infrastructure.
Sets and scales multi-department strategy for event-driven system design and cloud architecture using enterprise-authorized tools.
Establishes standards for microservices architecture, cloud deployment strategies, and automated testing.
Applies knowledge of cloud and infrastructure tools to drive cross-domain integration.
Utilizes enterprise-authorized automation capabilities for measurable performance improvements.
Provides leadership and high-level direction to engineering teams across multiple platforms.
Oversees operations across various business lines, ensuring alignment with strategic goals.
Acts as the primary interface with senior leaders and stakeholders, driving consensus across technical objectives.
Sets and scales multi-department strategy for agentic AI-enabled engineering and SDLC/TLM automation (using enterprise-authorized tools within the work environment) to drive firmwide objectives (speed, scalability, reliability, and cost-to-serve), including portfolio-level standards for AI-orchestrated delivery workflows, release governance, automated test modernization, resilience engineering, and incident response acceleration; establishes guardrails for validation, security, resiliency, traceability, and reuse.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to drive cross-domain reuse and measurable capacity unlock outcomes across departments.
Influences peer leaders and senior stakeholders across the business, product, and technology teams