Director of Software Engineering - Workforce ExperienceTechnology

If you are a software engineering leader ready to take the reins and drive impact, we’ve got an opportunity just for you.

As a Director of Software Engineering at JPMorganChase within Workforce Experience Technology (WxT), you will lead a critical, product-aligned engineering organization. The organization is responsible for building and operating a secure, scalable, cloud-native Talent Performance platform that underpins JPMorganChase’s career management and development function and serves the firm’s global employee base. You will own technology direction and execution across multiple teams. You will drive modernization and integration strategy, and partner closely with HR Product, Experience Design, Risk/Controls, and senior stakeholders to deliver measurable outcomes.

Job Responsibilities

• Set engineering vision and roadmap execution for the Talent Performance platform, balancing user experience, time-to-market, reliability, and control obligations.
• Lead multiple cross-functional engineering teams (full stack, backend, platform/integration, SRE/operations as applicable), including org design, hiring, coaching, performance management, and succession planning.
• Drive platform architecture and modernization, including cloud-native patterns, domain-driven service boundaries, API/event strategy, and reusable platform capabilities.
• Own end-to-end delivery and operations, ensuring production readiness, incident management, reliability/SLOs, observability, cost governance, and continuous improvement.
• Oversee integrations into HR workforce and compensation systems, workflow tooling, identity/access systems, analytics, and external SAAS vendors; ensure resilient integration patterns and strong data contracts.
• Establish and enforce engineering governance: secure coding standards, architecture review, SDLC controls, change management, audit readiness, and measurable engineering KPIs (availability, latency, defect escape rate, lead time, cost to serve).
• Partner with Product, UX, and HR stakeholders to translate strategy into outcomes, manage trade-offs, and ensure solutions solve real business problems.
• Manage third-party vendor and platform relationships: due diligence, technical fit, security posture, delivery oversight, and commercial/operational accountability (in partnership with sourcing and product).
• Champion a strong engineering culture grounded in inclusion, accountability, high standards, and continuous learning; promote responsible use of AI-assisted development with appropriate guardrails.
• Sets direction and governance for agentic AI-enabled engineering and SDLC/TLM automation within a technical area to drive measurable improvements in speed, quality, and operational outcomes (e.g., AI-orchestrated delivery workflows, release readiness controls, automated test modernization, and incident triage acceleration), while establishing guardrails for validation, security, resiliency, traceability, and reuse across teams.
• 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 and support capacity unlock initiatives at scale.

Required Qualifications, Capabilities, and Skills

• Formal training or certification on software engineering concepts and expert applied experience. In addition, experience leading technologists to manage, anticipate and solve complex technical items within your domain of expertise.
• Hands-on experience with modern full-stack engineering, including React/TypeScript and enterprise design systems.
• Production implementation experience with large scale Graph databases (e.g. Neptune, Neo4J, TigerGraph).
• Familiarity with industry standard cloud data persistence (e.g. PostgreSQL/Aurora). Strong knowledge of cloud-native engineering (e.g., AWS or equivalent), including reliability, security, cost management, and SDLC automation.
• Backend and API expertise with Java/Spring Boot, and strong GraphQL/REST API design, versioning, and governance practices. Experience delivering modern web and API platforms, including distributed systems, service-oriented architecture/microservices, and integration-heavy domains.
• Solid security and data fundamentals, including OAuth2/OIDC, service-to-service security patterns,
• Proven track record leading mission-critical platforms with high availability, strong operational excellence, and measurable outcomes.
• Demonstrated ability to influence and align senior business, product, and technology stakeholders, including communicating strategy, risks, and delivery status with clarity. Alongside, experienced people leader: hiring, mentoring, talent development, building inclusive/high-performing teams.
• Hands-on understanding of engineering controls and security: identity/access management, data privacy, secure SDLC, vulnerability management, and audit/compliance expectations.
• Experience leading adoption of agentic AI-enabled engineering practices (using enterprise-authorized tools within the work environment) across teams, including defining operating expectations (human-in-the-loop validation, quality gates), measuring outcomes, and ensuring secure handling of sensitive inputs/outputs.
• Strong understanding of responsible AI use and control expectations in engineering workflows, including data sensitivity, resiliency/security implications, and governance; ability to influence leaders on safe scaling patterns and reuse.

Preferred Qualifications, Capabilities, and Skills

• Experience with HR technology and Talent performance management workflows.
• Proven ability integrating talent/HR applications with enterprise HR suites (e.g., Oracle HCM or similar) and broader vendor ecosystems.
• Experience with event-driven architectures using Kafka/MSK, including schema evolution, idempotency patterns, and operational controls (e.g., DLQs).
• Strong platform engineering and delivery practices: CI/CD, automated testing strategies, IaC (Terraform/CloudFormation/CDK), containers (ECS/EKS), plus establishing standards (service blueprints, API/event contract governance, SLO/SLI frameworks) and responsible AI-assisted engineering practices (secure usage, quality gates, productivity enablement).