• 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

• 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 6 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 6 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.

Preferred qualifications, capabilities, and skills

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