Job Responsibilities
Develop and execute comprehensive program and project plans (scope, milestones, dependencies, technical interfaces, and delivery approach) to drive timely, predictable delivery across multiple workstreams
Own outcomes end-to-end; proactively surface issues, drive decisions, and implement corrective actions to protect delivery, quality, and stakeholder trust
Use enterprise-authorized AI capabilities within the work environment to accelerate program planning and risk synthesis (e.g., consolidating inputs into integrated plans, RAID, and executive-ready updates), validating outputs and handling data according to sensitivity requirements.
Identify and mitigate risks early; maintain actionable RAID and contingency plans to preserve momentum and delivery integrity
Drive execution rigor: ensure thorough planning, diligent follow-through, and continuous removal of blockers across teams
Partner with engineering, product, and business/risk stakeholders to define scope, objectives, deliverables, technical dependencies, and success metrics aligned to business goals
Use data and analytical reasoning to assess program health, identify improvement opportunities, and implement optimizations to increase efficiency and effectiveness
Champion strong problem-solving practices, bring the right stakeholders together, clarify roles and expectations, and drive resolution of complex cross-functional issues
Promote reuse-first, AI-assisted practices for program governance and reporting, ensuring human review and alignment to delivery standards.
Required qualifications, capabilities, and skills
5+ years of experience or equivalent expertise in technical program management, leading complex technology projects and programs in a large organization
Advanced expertise in stakeholder management, with the ability to establish productive working relationships and influence decision-making across cross-functional teams and clients
Advanced experience applying technical fluency to lead complex technology programs across shared platforms, services, and dependencies in large-scale environments
Proven track record of effectively managing resources, budgets, and influencing high-performing teams in a fast-paced, agile environment
Demonstrated proficiency in applying analytical reasoning and problem-solving techniques to break down business, technical, or operational objectives into manageable tasks and activities
Demonstrated technical fluency across software and system delivery lifecycles, architecture concepts, APIs and data flows, release processes, and production readiness
Proven ability to engage credibly with engineering teams on design dependencies, integration risks, and execution tradeoffs without owning direct engineering people management
Experience decomposing complex technical initiatives into executable plans, identifying dependency and failure points early, and driving cross-team mitigation to protect delivery outcomes
Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support program management workflows with strong validation habits and awareness of data sensitivity.
Ability to review and validate AI-assisted plans, risks, and recommendations before use, escalating when uncertain and following data handling expectations.
Preferred qualifications, capabilities, and skills
Demonstrated ability to operate as a trusted advisor to senior stakeholders, including executive sponsors, with concise and transparent communication
Experience driving continuous improvement in delivery practices (e.g., operational rigor, metrics, planning discipline, and lessons learned)
Relevant certifications (e.g., PMP, PMI-ACP, CSM/SAFe) or equivalent demonstrated agile program leadership
Prior hands-on background in software engineering or a comparable technical discipline, or equivalent experience as a highly technical TPM leading complex engineering programs
Experience with large-scale distributed systems, cloud or platform services, data ecosystems, or production operations environments
Ability to reason through end-to-end system functionality and translate technical context into program decisions, risk management, and executive communication
