Description:
The Transformation Manager will design, lead and embed technology change across Agile ways of working, AI-enabled productivity, and engineering excellence within a Technology portfolio. The role accelerates delivery quality and team productivity by co-creating OKRs and Activity Roadmaps with Portfolio Leads and Pod Leads, and by delivering coaching, tooling guidance and governance to ensure sustainable change. Pod Leads will be responsible for implementing the plan activities and achieving the objectives. Other resources may be allocated from other functional and shared service teams (e.g. Agile Coaches from Agile Services) and the Transformation Manager will oversee and allocate to specific tasks. The role will report into an The Head of Transformation for Marsh Tech.
We will rely on you to:
- Co-design transformation strategy for Agile, AI productivity tools and engineering excellence in partnership with SMEs, Portfolio Leads and senior engineering leaders.
- Translate strategic objectives into measurable OKRs and Activity Roadmaps for portfolios and teams.
- Partnering with Portfolio Leads and Pod Leads to agree the plans, timelines and measures of success.
- Act as an SME in at least one of the following areas: of Agile Ways of Working, Engineering Excellence and AI as tool for Tech productivity.
- Work with PDE to ensure that software delivery is lean, agile, smooth and efficient and gets software through from requirements to working in production.
- Focus is value creation, growth and serving customers.
- You will coach and / or mentor individuals or teams in new skills or techniques as needed.
Agile ways of working
- Define and propagate practical Agile approaches (Scrum, Kanban, team-of-teams, SAFe/Lean ways where applicable).
- Work with teams and leaders to ensure cadences (PI planning, sprint ceremonies, retrospectives), Agile metrics and continuous improvement.
AI tools for productivity
- Identify, evaluate and pilot AI tooling (LLM copilots, code generation, automated testing assistants, knowledge assistants) to boost developer productivity.
- Develop adoption playbooks, guardrails and security/privacy controls for safe AI usage.
Engineering excellence
- Drive improvements in CI/CD, test automation, observability, architecture hygiene, code quality, security by design and tech-debt management.
- Introduce engineering practices (SRE principles, automated release pipelines, trunk-based development) where suited.
Delivery & change management
- Run pilot programmes, manage rollouts, collect feedback and iterate. Use change management levers (training, coaching, communications, communities of practice).
- Ensure cross-functional dependencies are managed and blockers escalated.
Measurement & governance
- Define and track KPIs to measure initiative impact; surface regular progress reports to Portfolio Managers and leadership.
- Establish governance for OKR progress reviews and technical change controls.
Capability building & knowledge sharing
- Build internal capability via workshops, playbooks and mentoring; enable Portfolio Managers to steward ongoing change.
- Curate and maintain a library of patterns, playbooks and learnings.
Vendor & tool management
- Evaluate third-party tools, manage vendor relationships for AI and engineering tooling, and ensure integration with enterprise standards.
What you need to have:
Behavioural competencies
- Strong stakeholder management: influence senior leaders and cross-functional teams without direct authority.
- Effective communication and facilitation: run workshops, consensus sessions and executive briefings.
- Coaching and mentoring: ability to upskill teams and leaders through hands-on coaching.
- Change leadership: pragmatic change delivery focus, resilience and bias for action.
- Analytical problem solving: data-driven decision making and pragmatic trade-off management.
Technical Skills And Domain Knowledge
- Deep familiarity with Agile frameworks (Scrum, Kanban; experience with SAFe or team-of-teams is an advantage).
- Practical experience with OKR design, cascading and measurement.
- Knowledge of AI tooling for software engineering and productivity (LLM copilots, code generators, test generation tools, knowledge bases) and an understanding of their strengths/risks.
- Strong understanding of modern engineering practices: CI/CD, automated testing, test-driven development, trunk-based development, DevOps/SRE, observability and security-by-design.
- Hands-on exposure to tools such as ADO, Confluence, Git/GitHub/GitLab, CI servers (Jenkins/Circle/GHA), observability (Prometheus/Grafana/New Relic), and common AI/ML toolchains; able to evaluate tooling fit.
- Data-literate: comfortable defining and using metrics/dashboards to measure impact.