Lupa Pets - Data Engineer
Requirements
• 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: • 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
Responsibilities
• 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
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