Engineering Programme Manager

About Moneybox

At Moneybox, our mission is to give everyone the means to get more out of life. We're guided by our belief that wealth isn't about the money, it's about the means to more - more freedom, opportunities, possibilities, and peace of mind. Moneybox is an award-winning wealth management platform, helping over one and a half million people build wealth throughout their lives, whether they’re saving and investing, buying their first home, or planning for retirement.

Moneybox engineering delivers change through multiple cross-functional squads and a dedicated Architecture team, supported by strong Tech Leads and Engineering Managers, and operating on a structured 8-week delivery cycle. We serve more than 1.5M customers and run a live service handling over 20M API requests a day. We intentionally run our engineering management lean and hands-on. This creates a need for departmental-level, cross-team programme management: someone who owns the bird's-eye view of delivery, maps dependencies across squads, and works with teams to course-correct so that we deliver predictably every cycle. You are not coming in to fix a broken engineering culture or manage engineers - our Managers and Tech Leads have that covered. You are coming in to solve the problem of orchestration at scale: how many teams collaborate effectively to deliver against the company roadmap.

Responsibilities

• End-to-end cycle orchestration. Own the 8-week operational cadence in partnership with the Change team. Manage the flow of work from shaping through to squad execution, focusing on measurable outcomes and total delivery visibility.
• Provide coaching and challenge to Squad Delivery Leads.
• Centralised reporting. Build and own the source-of-truth view of roadmap health. Translate team-level progress into clear, concise status reporting for executives and non-technical stakeholders.
• Risk and dependency mapping. Proactively identify cross-squad and architectural dependencies before they block progress, and track them explicitly through the cycle.
• Data-driven course correction. Use data to flag delivery risks early, facilitate tough scope and resourcing conversations with Product and Tech Leads, and act as the primary escalation point to resolve cross-team friction.
• Lean process leadership. Partner with Engineering leadership to continuously refine our agile delivery processes without adding bureaucracy.
• Financial and operational governance. Own the data pipeline for engineering workloads: repeatable project tracking and a practical approach to categorising engineering spend that supports financial planning, capacity decisions, and future team sizing - without getting lost in the weeds.
• Complex incident coordination. Provide leadership, operational coordination, and stakeholder communications for high-severity, cross-functional incidents.

What we are looking for

• Experience at scale. 5+ years as a Programme Manager, TPM, or Agile Delivery Lead managing complex portfolios spanning multiple parallel teams in a fast-paced software environment.
• A well-developed Twinge. You have shipped enough software, with enough teams, that your experience speaks to you when an update is hiding a problem.
• Structured, strategic problem solving. You can take vague direction and turn it into concrete outcomes.
• Technical literacy. You don't need to write code, but you must hold your own in a room with Tech Leads and Architects.
• Pragmatic delivery kit bag. Deep experience running structured, time-boxed delivery models (e.g. 6- to 12-week cycles, ShapeUp, scaled agile, etc.).
• Data and tooling proficiency. Advanced proficiency with project management tooling (Jira, Linear, Asana, etc.) and a proven ability to build clean, scannable reporting.
• Clear communication. Able to synthesise highly technical detail into concise, impact-focused updates for executive leadership.

What success looks like in 6 months

• No blind spots. Every stakeholder knows what is on track, what is at risk, and why - without needing to ping individual engineering managers.
• Predictable cycles. Our 8-week cycles achieve a high predictability rate, with scope adjustments happening proactively mid-cycle rather than as a surprise in the final week.
• Trouble is found early. Risks surface because you sniffed them out, not because they materialised.
• Seamless dependencies. Cross-team dependencies are mapped cleanly during planning and tracked explicitly week over week.
• Trusted delivery data. Engineering workload data is captured consistently enough to support team sizing decisions and financial planning.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.