Zopa2mo ago

Senior Data Engineer

London · (london)Employee - Permanentsenior

Data EngineeringData EngineerData & AI

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Quick Summary

Key Responsibilities

engineers are expected to keep learning, and we actively support time spent improving skills and deepening expertise. You love shipping great data products and reliable pipelines,

Technical Tools

Data EngineeringData EngineerData & AI

Our Story

Hello there. We’re Zopa.

We started our journey back in 2005, building the first ever peer-to-peer lending company. Fast forward to 2020 and we launched Zopa Bank. A bank that listens to what our customers don’t like about finance and does the opposite. We’re redefining what it feels like to work in finance. Our vision for a new era of banking puts people front and centre — we’ve built a business that empowers everyone to aim high, every day, to move finance forward. Find out more about our fantastic offerings at Zopa.com!

We’re incredibly proud of our achievements and none of it would be possible without the amazing team here. It’s not just industry awards we’re winning, we’ve also been named in the top three UK’s Most Loved Workplaces.

If you embrace unconventional challenges, are unafraid to think differently and are driven to make an outsized impact, you’ll thrive here at Zopa, so join us, and make it count. Want to see us in action? Follow us on Instagram @zopalife

The team:

The Data Engineering team are responsible for designing, building and running the complex data pipelines that the bank relies on. We combine skills in software engineering, data analysis, and operations and partner with teams across the company to solve varied, challenging problems. We value collaboration, pragmatism and continual learning over individual heroics.

This is a chance to join Zopa’s Data Engineering team at a genuinely interesting point of scale and maturity. You’ll be building and evolving the data platforms that underpin decision-making across the entire bank — from customer behaviour and product analytics to time-critical use cases like fraud, where minutes genuinely matter.

You’ll collaborate to design and build resilient data pipelines and warehouse solutions, owning their quality, reliability, and end-to-end delivery.

Requirement gathering with Product teams, Analysts and business stakeholders inside and outside of the team, through pairing and peer reviewing on tasks, to developing tested, well-documented data workflows running in production

Ensuring data architecture and modelling decisions are documented and understood, taking an active part in the team's operational responsibilities

Have a voice in all our ceremonies from retrospectives, refinement sessions, architectural planning sessions through to social activities, helping to shape how we work and continuously improve our data capabilities

We take development seriously: engineers are expected to keep learning, and we actively support time spent improving skills and deepening expertise.

You love shipping great data products and reliable pipelines, not just writing elegant code

You are an experienced Senior Data Engineer, who works with Python and SQL, and is comfortable designing and building data pipelines end‑to‑end

As an Engineer, you're interested in the business questions behind the data, not just the technical requirements

You feel comfortable in a fast-moving environment and are ambitious about the speed, reliability and observability of your data workflows

You care deeply about data quality, testing and documentation, and take a systematic approach to ensuring trustworthy, well-modelled datasets

You've worked on large-scale data platforms or warehouses, with proven experience handling complex data models and high‑volume datasets in production

You enjoy partnering with analysts, analytics engineers and product teams, and you're motivated by enabling others to make better decisions with data

You are comfortable with ambiguity in a rapidly evolving environment, and are excited by evolving architectures and technologies

You are a team player who thrives in a humble, supportive environment, are happy to pair and help others, and are just as comfortable asking for help yourself

Experience of data engineering/analytics in a regulated environment – e.g. finance, retail or healthcare

Hands-on experience of technologies such as AWS, Terraform, Spark, DBT or Airflow

Exposure to Databricks or Snowflake