What you'll doThe
Director, Engineering Performance & AI Metrics defines how the organization measures engineering and agile delivery performance at scale, with a specific focus on how "performance" changes as AI becomes embedded in agile ways of working. This role is accountable for establishing an Agile & AI performance measurement practice, including the standards, tooling, governance, visualization and story-telling, team structure, and operating routines needed to measure delivery flow, predictability, quality, business value, and the impact of GenAI and automation.
Core Accountabilities
- Establish and lead the Agile & AI performance measurement practice, including its operating model, governance, standards, team capability, and roadmap.
- Define enterprise measurement frameworks for agile delivery and AI-enabled ways of working, covering flow, predictability, quality, value realization, team health, adoption, and productivity impact.
- Augment traditional agile performance metrics with new KPIs that reflect total system health in an agentic landscape, including human-AI collaboration, automation effectiveness, work orchestration, quality signals, risk, governance, and team sustainability.
- Own the Engineering performance analytics platform strategy and roadmap, including data quality, governance, tooling integration, investment recommendations, and scalable visibility across engineering and agile delivery.
- Translate performance data into executive-ready stories that connect trends, risks, systemic bottlenecks, investment decisions, delivery trade-offs, transformation progress, and improvement opportunities.
- Partner across engineering, delivery, finance, transformation, and agile leadership to connect performance insights to business outcomes and operating model decisions.
- Ensure metrics are used responsibly, with enough context to support learning, transparency, and continuous improvement rather than activity tracking or simplistic team comparisons.
- Build and lead a high-performing performance measurement and insights practice, including role design, talent development, delivery model, and capability roadmap.
What you bring- 10+ years of experience in agile delivery, delivery excellence, transformation, product operations, performance measurement, or related roles.
- Ability to assess AI adoption and impact responsibly, including metric limitations, false precision, quality, risk, governance, and team health.
- Strong understanding of agile operating models, scaled delivery practices, agile metrics, and executive performance story-telling to support decision-making
- Deep expertise in engineering productivity and delivery performance frameworks such as SPACE and DORA, with demonstrated ability to adapt them to enterprise-scale Agile and AI-enabled delivery environments.
- Demonstrated ability to establish enterprise standards and frameworks, analysis structure and strategic visualization, and insight routines that drive practical improvement.
- Proven ability to translate complex engineering, delivery, and AI adoption signals into executive-level insights that influence prioritization, investment, risk management, and operating model decisions, using enterprise delivery and analytics platforms including Power BI, Atlassian, and other engineering tools
- Strong practical understanding of GenAI, agentic AI, automation, and AI-enabled software delivery, including how these capabilities change work patterns, delivery flow, developer experience, quality, productivity, and value realization.
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We may use artificial intelligence tools as part of our recruitment process to assist in the initial screening of resumes. All hiring decisions, including candidate evaluation, selection, and disposition, are made by human recruiters.
