Global Delivery Manager

• Execution Leadership: Convert Product and engineering priorities into sequenced delivery commitments, operating plans, and follow-through across global product engineering teams. Maintain a clear view of what is in flight, what is next, and what requires a decision or escalation.
• Capacity and Backlog Planning: Partner with Product Owners, engineering leads, and the Director to forecast capacity, balance demand across onshore and offshore teams, sequence backlog work, and make trade-offs visible before commitments are made.
• Engineering Flow and Metrics: Establish practical flow measures such as throughput, cycle time, work in progress, predictability, escaped defects, deployment frequency, and change failure trends. Use metrics to improve the system, not to create activity-based reporting.
• AI-Assisted and AI-First SDLC Adoption: Help teams adopt approved AI-assisted engineering practices across discovery, coding, testing, documentation, and operations. Set expectations for effective and responsible use of AI while preserving engineering judgment, code ownership, review discipline, security, privacy, and accountability.
• Quality and Release Readiness: Drive CI/CD adoption and automated quality, security, compliance, and change-control gates. Coordinate release readiness, production quality, validation, rollback preparedness, and post-release follow-through.
• Dependency, Blocker, and Risk Management: Surface cross-team dependencies, delivery risks, and blockers early; assign owners; drive resolution; and escalate decisively when issues threaten commitments, quality, security, or customer impact.
• Cross-Functional Partnership: Work closely with Product Owners, Centers of Excellence, Architecture, Infrastructure, Security, Operations, and Client Services to align execution, resolve handoffs, and maintain a shared view of delivery and operational health.
• Vendor and Global Delivery Management: Set expectations and manage outcomes for vendors and offshore partners, including capacity, quality, delivery commitments, communication, service levels, and continuous improvement. Lead effectively across time zones and cultures.
• Regulated Healthcare Delivery: Embed awareness of HIPAA, information security, privacy, auditability, change control, and other regulated healthcare expectations into planning, delivery, release, and operational practices.
• Team Leadership and Coaching: Coach engineering leads and engineers, strengthen team-level ownership, clarify decision rights, and build autonomous teams that can plan, deliver, operate, and improve their products with appropriate support and guardrails.
• Continuous Improvement: Facilitate regular inspection and improvement of delivery practices, team interfaces, tooling, and ways of working. Turn recurring issues into durable process, capability, or system improvements.

• Bachelor's degree in Computer Science, Software Engineering, Information Technology, or a related field, or equivalent relevant experience. Master's degree preferred.
• At least 7 years of experience in software or product engineering delivery, including significant experience leading global or distributed teams.
• Demonstrated experience translating product and engineering priorities into predictable execution across multiple teams, products, or value streams.
• Experience with capacity planning, backlog management, delivery commitments, engineering metrics, release readiness, and production quality.
• Experience leading vendors or offshore delivery partners and working across time zones, cultures, and organizational boundaries.

Knowledge, Skills, and Abilities

• Strong understanding of modern software and product engineering delivery, including Agile and Kanban practices, flow-based planning, and team-level ownership.
• Proficiency with delivery and work-management tools such as Azure DevOps, Jira, or comparable platforms; able to create useful views of backlog, capacity, dependencies, risks, and commitments.
• Ability to define and interpret engineering delivery and quality metrics, using evidence to improve predictability, flow, and customer outcomes.
• Familiarity with AI-assisted engineering and AI-first SDLC practices, including appropriate human review, secure use, data protection, and accountable adoption.
• Working knowledge of CI/CD, automated testing, DevSecOps practices, release controls, observability, and production incident or defect follow-through.
• Strong leadership and coaching skills, with the ability to develop leads, empower autonomous teams, and build trust across onshore, offshore, and cross-functional partners.
• Strong dependency, risk, escalation, communication, and problem-solving skills; able to make issues visible and drive them to closure.
• Understanding of regulated healthcare environments, including HIPAA, privacy, security, auditability, and change-control expectations.