Data Engineer

Join a small team building a 0-1 product, bootstrapped, but backed by the resources of an established, profitable parent business. Your work ships fast and gets used immediately by the team you sit alongside, so you test, iterate, and feel the impact within days rather than quarters. There's a capable go-to-market function ready to take what you build to market, and significant room to scale quickly.

You get real ownership, real equity, and a genuine voice in the roadmap, in an AI-native team that treats the latest tools as a daily multiplier rather than a novelty.

About us

We're transforming a services-led business into an AI-native one, with technology genuinely at the core rather than bolted on. A small, high-leverage team, a director, product managers, product engineers, a data engineer, and a generalist, has been put in place to build this out. The team is growing as the product becomes revenue-generating. We test, validate, and iterate quickly.

What we're building

• An internal data platform: a centralised data lake, copilot, and dashboard product that stitches together operational data, historical records, and enrichment from first-, second-, and third-party sources
• Internal tooling that removes manual admin from day-to-day operations
• An external product: an AI-driven matching platform already live with paying customers

The role

This hire sits between our product engineers and our data engineering foundation. You'll move fluidly between the two: one week shaping an internal dashboard or workflow, the next hardening a data pipeline or evolving the schema everything downstream depends on.

Expect roughly a 50/50 split between product-surface work and data work. On the product side you'll build interfaces, copilot-style workflows, and AI-assisted experiences. On the data side you'll own pipelines, data quality, and modelling, the foundation layer every output relies on. A degraded pipeline is a commercial issue before it's a technical one, and you'll treat it that way.

Above all, this is an engineering hire. We're optimising for someone who owns work end-to-end and needs light oversight, not hand-holding. You ship to 100% when it matters and a deliberate 80% when speed matters more, and you know the difference.

What you'll do

• Build product surfaces: interfaces, dashboards, and AI-assisted workflows that turn structured data into useful actions for internal and external users
• Own the data foundation: build and maintain pipelines ingesting data from multiple operational systems, monitored, documented, and alerting on failure before it affects downstream outputs
• Guard data quality: completeness checks, anomaly detection, freshness monitoring, surfaced before they affect anything built on top
• Model the data: design and evolve the schema so it serves both structured reporting and LLM reasoning access
• Build integrations and enrichment: maintain system integrations and evaluate third-party enrichment sources
• Build trustworthy AI experiences: guardrails, transparency, and user control, so AI features feel reliable, not gimmicky
• Ship fast and learn: deploy regularly, instrument adoption, iterate on real usage

About you

• Trusted to deliver: you own work end to end with light oversight
• Strong engineer, language-agnostic: real craft and good fundamentals. We don't have a hard Type Script/React requirement, a good engineer is a good engineer, what matters is genuine ability to move across stacks. Strong production Python and SQL are the core
• Genuine data depth: you've built and maintained production data pipelines at scale, not just designed them. Cloud data infrastructure experience preferred
• The shape we're looking for: ideally a genuine multi-year product or front-end chapter before moving into data, someone who's built real product and then developed data depth. Roughly 4-6 years building and shipping software
• AI-native, the critical signal: you use AI and coding agents as a force multiplier in real production settings, not a substitute for thinking. A plus if you build with AI in your own time
• Comfortable with ambiguity: you translate business outcomes into technical work without needing a complete spec
• Collaborative and clear: you document clearly and communicate well across the team
• Bias toward simple systems: simple, maintainable systems over clever ones

Stack: Python, SQL, PostgreSQL, Supabase, Google Cloud, and React/Next.js, Node.js/Type Script on the product side.