Solutions Engineer

About the Role

We are a fast-growing, FinTech company looking for a talented and enthusiastic individual to join our team. We are expanding, making this a perfect position if you would like to have a significant impact on our company’s growth and develop your role and career as the business evolves. You will join a team where your ideas will be welcomed and valued.

You'll work at the boundary between the Insignis platform and the wider sector — partner banks, distribution and white-label partners, introducer and agent firms — and alongside our production engineering teams. Your job is to compress the time from "there's a problem or an idea worth pursuing" to "there's a system handling real money or real users," and to do it so that the teams who'll own it long-term can absorb it without inheriting a liability. You'll spend your time roughly equally between building and being in the room with the people who have the problem.

What You'll Do

• Embed directly with partners and internal product teams to understand the actual problem — often before it's written down as requirements — and translate it into something buildable.
• Rapidly prototype: integrations against partner APIs, white labelled apps, new deposit-product flows, partner-onboarding tooling, internal capability. At this stage you optimise for speed of learning over architectural purity, and you're explicit about that being a deliberate choice.
• Take the prototypes that prove out into early production — enough robustness, observability, and compliance posture to handle live flows — in close partnership with the production engineers who will own them.
• Own the handoff. Instrument it, document it, de-risk it. A prototype that production can't absorb is a failure, not a success.
• Work inside Insignis's regulatory and operational guardrails —Consumer Duty, FCA SYSC, DORA operational resilience, UK GDPR — and know which constraints bind at prototype stage versus hardening. Pull compliance and ISMS in early, not as a gate at the end.
• Feed learnings back into the platform: what's repeatedly missing, what should become a reusable capability, where the next-generation architecture should go.