Director of Software Engineering- HR Technologies

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 JPMorgan Chase within HR Technology – Talent Acquisition, you will lead a critical product-aligned engineering organization responsible for building and operating a secure, scalable, cloud-native Talent Acquisition platform. You will own technology direction and execution across multiple teams, 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 Acquisition 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 TA 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 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.

• Proven track record leading mission-critical platforms with high availability, strong operational excellence, and measurable outcomes.

• Experience delivering modern web and API platforms, including distributed systems, service-oriented architecture/microservices, and integration-heavy domains.

• Strong knowledge of cloud-native engineering (e.g., AWS or equivalent), including reliability, security, cost management, and SDLC automation.

• Demonstrated ability to influence and align senior business, product, and technology stakeholders, including communicating strategy, risks, and delivery status with clarity.

• Hands-on understanding of engineering controls and security: identity/access management, data privacy, secure SDLC, vulnerability management, and audit/compliance expectations.

• Strong people leadership: hiring, mentoring, talent development, building inclusive/high-performing teams.

• 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 Acquisition workflows, including ATS/CRM platforms, candidate pipelines, interview scheduling, offers, background checks, and onboarding.

• Proven ability integrating talent/HR applications with enterprise HR suites (e.g., Oracle HCM or similar) and broader vendor ecosystems.

• Familiarity with modern full-stack engineering, including React/TypeScript and enterprise design systems.

• Backend and API expertise with Java/Spring Boot, and strong GraphQL/REST API design, versioning, and governance practices.

• Solid security and data fundamentals, including OAuth2/OIDC, service-to-service security patterns, and data persistence (PostgreSQL/Aurora; graph stores like Neptune where applicable).

• 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).