Engineering Manager

Trust Flight builds software that aviation operators around the world rely on to keep flying safe, for an international customer base with an unusually varied set of problems. This role exists to build and sustain a high-performing team of six to eight engineers: coaching each individual, holding a consistent standard of performance, and owning the process, testing, tooling and infrastructure the team depends on.

It is a people and systems leadership role rather than a technical leadership one. Product and the technical leads decide what gets built and how; the Engineering Manager owns the conditions that allow them to build it well and quickly. The postholder is one of four Engineering Managers in a Product, Design and Engineering department of around 40 to 50 people, which is growing and maturing quickly.

There is considerable scope here to shape how engineering works across it.,

• Raise the performance bar with every hire and every promotion. Treat coaching as a core duty of the role, and upskilling as a standing remit: develop engineers through regular 1:1s, direct feedback, stretch assignments and explicit development plans, and grow technical leads as well as individual contributors.
• Manage performance fairly and decisively. Set explicit expectations, recognise strong work, raise problems early and allow runway to respond, and address sustained underperformance promptly.
• Act as the chief unblocker for the team across tooling, infrastructure, testing and process, and own the team's infrastructure-as-code estate. Identify the genuine constraints on throughput, including code review latency, slow or flaky tests, handoffs, approval gates and unreliable environments. Remove them, ensure the team can build, test, deploy, observe and recover quickly and safely, and evidence the improvement in flow metrics.
• Own testing standards and the consistency of quality across the team, including the definition of "tested", the criteria that gate a release, and the management of coverage and test flakiness. Quality should not vary according to who wrote the code.
• Architect cross-team developer tooling and drive consolidation of the technology estate, jointly with peer Engineering Managers and the Director of Engineering. Build the case for moving from a varied stack onto fewer, better-supported technologies. Establish paved roads and automated guardrails that teams adopt on merit, and sequence the change so that it strengthens delivery.
• Work with the technical leads to maintain an accurate and current picture of technical debt, quality and process constraints. Translate it into risks and opportunities the Product Manager can prioritise, so that the balance between product output and technical excellence is a deliberate decision.
• Lead the team's adoption of AI-assisted development. Set the standards for where and how AI is used, ensure AI-generated code is understood and accountable to a named engineer, guard against data, licensing and security risk, and maintain an evidence-based view of its effect on the team.
• Help establish consistent engineering practice across Trust Flight with the other Engineering Managers. No shared approach to levelling, calibration, feedback and hiring yet exists, and building one is part of this role, so that an engineer's experience does not depend on which manager they report to., Trust Flight is an AI-first organisation. Every role is expected to actively engage with AI tools and digital ways of working to drive efficiency, quality, and innovation.

• Set and maintain the team's standards for AI-assisted development, covering which tools are approved, where they may and may not be used, and how their output is reviewed.
• Ensure AI-generated code entering the codebase is understood and accountable to a named engineer, and that review capacity scales with the volume AI produces.
• Apply AI to strengthen testing and documentation, ensuring that AI-written tests validate the requirement and do not simply confirm the behaviour of the implementation.
• Establish guardrails against the specific risks of AI-assisted engineering: leakage of customer or regulated data into model context, licensing…