Summary
✨ AI‑Generated
Lead delivery performance across a portfolio of engineering, quality, and product teams. You will drive value from idea to production, communicate delivery health and risks to leadership, coach team leads, facilitate collaboration, resolve conflicts, and use data and AI-augmented practices to improve throughput and predictability.
Highlights
Strategic delivery leadership across multiple engineering, quality, and product pods, with autonomy to adapt delivery approaches, improve throughput, reduce waste, coach teams, and use data and AI to enhance performance.
Description
Job DescriptionThe Portfolio Lead is accountable for delivery performance across a sub-domain of 4-6 pods (Engineering, Quality, Product).
They lead the flow of value from idea to production, ensuring pods deliver with pace, predictability and understanding to outcomes.
They combine hands-on delivery leadership with AI-augmented ways of working and data-driven decision-making.
They do not prescribe a single framework - instead they adapt approaches that best serve each pod's context.
They partner with Engineering and Product leadership to reduce waste, improve throughput and help pods achieve Autonomy, Accountability and Agreement to value outcomes.
What you will do
Pod Performance & Accountability
You are responsible for pod delivery performance across the sub-domain.
You will lead and communicate delivery health, risks and performance to leadership
Ensure pods operate as, self-organising units delivering value outcomes
Support pod leads with coaching, facilitation and conflict resolution
Ensure cross-pod agreement on priorities, dependencies and shared goals
Facilitate communication and collaboration between pods to promote knowledge sharing
Problem Solving
You will be an expert; deploy root cause analysis, systems thinking and structured techniques to analyse and guide out resolutions.
Be an escalation point for cross-pod blockers and systemic impediments
Identify patterns across pods that indicate deeper organisational or process issues
AI-Augmented Delivery
Champion AI agent adoption across pods - including coding agents, test generation, story creation, board health and deployment automation
Identify opportunities where AI can reduce toil, accelerate delivery or improve decision-making
Apply AI to improve delivery effectiveness, including workflow insights, risk prediction, and automation of reporting
Track and demonstrate the impact of AI on delivery performance
Flow & Efficiency
Monitor and improve delivery flow (lead time, cycle time, WIP, throughput) using Kanban and lean principles
Ensure WIP limits are respected and surface ageing items through Board Walks
Identify bottlenecks, ageing work and sources of delay across pods.
Improve through regular assessment and experimentation.
Support teams in breaking down work into deliverable, value-driven increments
Agile Ways of Working
Support and evolve the agile operating model for a pod-based environment focused on pace, not ceremony.
Adapt delivery approaches (Kanban, flow-based, lean) appropriate to each pod's needs
Ensure consistency of practice across pods without being prescriptive
Promote outcome-driven delivery vs output-driven activity
Guide teams to operate with autonomy and accountability
Metrics & Reporting
Define and maintain delivery metrics: cycle time, flow efficiency, lead time of initiative, PDLC compression, dependency reduction, rework rate, team satisfaction, and AI effectiveness
Build dashboards (cumulative flow diagrams, throughput charts, WIP ageing) that provide leadership visibility and lead informed decision-making
Analyse performance data to identify trends, patterns and areas for improvement
Use data to facilitate conversations and coach pods — not just track output
Stakeholder Management
Partner with Engineering and Product leadership at sub-domain level
Represent delivery health and risks in leadership forums
Build trusted relationships that allow fast decision-making
Communicate delivery performance to both technical and non-technical audiences
Commercial Awareness
Understand the commercial context of the sub-domain
Connect delivery decisions to revenue, cost, and customer impact
Support prioritisation trade-offs with a business lens
Capacity Planning
Forecast capacity across pods based on team composition, skills mix and upcoming demand
Identify resourcing gaps and support hiring/allocation decisions
Balance workload across pods to prevent burnout and underutilisation
Support realistic planning by ensuring pods have clear understanding of available capacity
Risk & Dependency Management
Manage risks through regular assessment and execution of controls
Ensure cross-pod dependencies are visible, tracked and managed
Track dependency cost (time blocked, items affected) and lead mitigations such as contract-first development and feature flags
Support issue escalations, ensuring intervention by the dependency escalation protocol
Coach & Capability Development
Provide hands-on coaching at pod level
Build capability in workflow management, flow thinking, estimation, planning, and data-led decision-making
Support teams in understanding and improving their own performance
Strengthen a culture of continuous improvement and learning
Outcome & Value Focus
Align delivery to always ensure outcomes and value.
Help teams understand the why behind their work and how their outputs contribute to value
Challenge work that does not link to outcomes
Support measurement of delivery against OKRs and value indicators
Centre of Excellence (CoE) Contribution
Be an active contributor to the CoE
Share best practices, delivery insights and improvement opportunities from pods
Support adoption of standards, playbooks and tools
Help evolve delivery practices based on experience