• Turn developer practices into engineering practices

  • Move us beyond individual developer habits to engineering discipline that holds at organisational scale — and at the change volume that agentic delivery produces.
  • Codify practice into the paved road: templates, scaffolds, pipelines, linters, policy-as-code, automated checks, and agent instructions, so the right thing is the easy thing for humans and agents alike.
  • Define, document, and continuously evolve the engineering standards that apply across Defaqto Technology — code review, testing, branching, observability, security by design, documentation, and definition of done.
  • Hold the line on quality in a way teams welcome: coaching, pairing, and reviewing rather than policing. Be our evangelist for software craftsmanship, and help raise the hiring bar to match it.

• Take hands-on technical ownership of the problems that matter most

  • Own the initiatives that carry the most technical risk or the most upside, end to end — architecture, build, launch, and the operational reality afterwards. What that is will change as our priorities do; the level at which you operate will not.
  • Design platforms and integration surfaces for both the consumers we have and the ones we are gaining: human developers and AI agents. Machine-readable capability discovery, unambiguous tool definitions, MCP servers, and other agent-facing interfaces where they fit, semantics an agent can reason over, predictable errors, idempotency, and rate limiting that behaves sensibly under agentic access patterns.
  • Build data governance into the technology rather than into a policy handbook: entitlements and licensing enforced at the point of access, per-consumer scoping, lineage and provenance carried with the data, and full audit of which human or agent accessed what, and when.
  • Make quality and trust observable — data contracts, freshness and quality signals exposed to consumers, and semantics clear enough that a consumer cannot silently misread a rating or a field.
  • Care about the consumption experience end to end: self-service onboarding, keys and sandboxes, SDKs, documentation as a product, usage analytics, versioning, and deprecation. Treat time to first successful call, by a person or an agent, as a metric you own.

• Partner with Product to add technology value

  • Act as the technical validation point for product ideas, pressure-testing feasibility, cost, risk, and sequencing early, before commitments are made.
  • Bring technology-led opportunities to the table: things our data, platform, and AI capability make possible that Product had not thought to ask for.
  • Translate between commercial intent and technical reality in both directions, without diluting either.

• Guide squads and evolve the tech stack

  • Guide multiple teams within your squad towards sound, well-supported technology choices, and away from novelty for its own sake.
  • Own continuous improvement of the tech stack with clear, valued outcomes: every material change carries a stated benefit, a measure, and a migration plan.
  • Lead the architecture decisions that cross team boundaries, document them as ADRs, and keep our technical debt position honest and visible.

• Be a strategic and tactical sounding board

  • Act as trusted counsel to the Head of Engineering, the CTO, and the wider leadership team on both long-range strategy and this week’s trade-offs.
  • Represent engineering reality in commercial, regulatory, and roadmap conversations.
  • Stay hands-on — build, review, and verify on the work that matters most.

What you'll need to succeed:

Essential requirements:

• Substantial commercial software engineering experience, including time at staff, principal, or equivalent level, with a track record of technical influence well beyond your own team.
• Deep, current, hands-on engineering ability in a modern stack, with the breadth to stay credible outside your strongest language. Our core stack is a mix of.NET / C# and TypeScript on Azure, Python, and PHP, with SQL and modern data tooling.
• Real, evidenced experience leading agentic and AI-assisted development in a production engineering organisation — not just personal use of an assistant. You can show how you changed a team’s practices, what you measured, and how you kept quality and accountability intact.
• A clear point of view on the engineering practices that make AI-led delivery work: specification and context engineering, test-first contracts, agent-readable codebases, and review as a first-class discipline.
• Demonstrable ownership of engineering standards and practices across multiple teams: you have written them, embedded them in tooling, and actually changed behaviour with them.
• Strong API and integration engineering: designing, versioning, securing, and operating interfaces consumed by other people’s systems — and increasingly by their agents — with real care for the consumer experience around them.
• Treats data governance as an engineering problem: entitlements, licensing, lineage, provenance, and auditability enforced in the technology rather than asserted in a policy document.
• Sound distributed systems and cloud architecture judgement — resilience, observability, performance, cost, and operability, not just diagrams.
• Excellent influencing and communication skills: you build consensus across teams and can hold your own with product, commercial, and executive audiences.
• Commercial instinct — you tie technical work to outcomes the business recognises and can measure.
• A mentoring track record: senior engineers have become materially better because they worked with you.
• Comfort with a moving remit. You are motivated by owning whichever problem is hardest right now, not by owning one system indefinitely.

Desirable requirements:

• Experience building or operating agent-facing integration surfaces — MCP servers, tool-calling interfaces, or similar.
• Financial services, fintech, or regulated data experience, and comfort treating FCA and UK GDPR / ICO expectations as design constraints rather than afterthoughts.
• Experience with data products, business intelligence, or analytics platforms — data modelling, contracts, quality, lineage, and entitlements.
• Experience of a developer portal, API marketplace, or platform-as-a-product.
• Platform engineering and developer experience work: CI/CD, internal developer platforms, golden paths, policy-as-code, and self-service tooling.
• Event-driven architecture, and experience of legacy modernisation or migration programmes.
• Contribution to the wider engineering community — open source, writing, speaking, or standards work.

Your approach to work:

• Pragmatic. You optimise for outcomes and maintainability, not for elegance