In this role, you will:
- Design and build data pipelines from raw ingestion through to clean, modelled, production‑ready datasets that engineering and product teams rely on
- Own data quality across your domain, defining standards, instrumenting checks, and resolving failures with urgency
- Monitor pipeline health in production and respond to issues proactively, not reactively
- Collaborate with engineering and product to understand data requirements and turn them into robust, scalable solutions
- Help define and evolve the architecture of our data platform as we scale across new geographies and product surfaces
- Document your work clearly so the team can understand, extend, and maintain what you build
Your background looks something like:
- Multiple years of hands‑on data engineering experience in a professional setting
- Strong Python skills and a demonstrated ability to build pipelines from scratch, not just extend existing ones
- Solid knowledge of data modelling and practical experience with Apache Spark, AWS services, Docker, and Airflow
- Experience in a startup or fast‑growth environment where you’ve made pragmatic decisions under uncertainty
- Scala experience (nice to have, we’re happy to invest in teaching it if your foundations are strong)
- Familiarity with dbt, Delta Lake, or similar modern data transformation tooling (nice to have)
- Exposure to ML pipelines or feature stores (nice to have)
As a person, you:
- Have a proactive, ownership‑oriented mindset. You don’t wait to be told something is broken
- Take genuine pride in craft and correctness, and hold yourself to a high bar
- Collaborate well with engineering teams who consume your data and communicate clearly across functions
- Thrive in environments where you’re expected to build from scratch, not inherit a tidy queue of tickets
- Are excited to work in‑person from our Paddington, London HQ (or equivalent location)
What does success look like in 6 months?
- You’ve built and shipped at least two meaningful pipelines end‑to‑end
- You own a defined area of the data platform and are making architectural calls with confidence
- The team trusts your judgement without needing to review every decision
