Data Engineer

Technical Delivery

Build, maintain, and improve data pipelines and related systems.

Follow and help uphold best practices in data engineering, including testing, CI/CD, observability, and infrastructure as code.

Take part in code reviews and team knowledge sharing.

Platform & Data

Build and maintain data pipelines, warehouses, and streaming systems within our existing architecture.

Ensure data is modelled and structured to meet the needs of analytics, data science, and operational use cases.

Contribute to improvements in our data systems and tooling.

Collaboration & Delivery

Work with teams across the business to understand requirements and turn them into reliable technical solutions.

Help identify opportunities for optimisation and tool improvements.

Deliver against the technical roadmap for data engineering.

You’re a hands-on engineer who enjoys solving problems and building reliable systems. You bring solid technical fundamentals, a pragmatic mindset, and a collaborative approach to everything you do.

What You Bring:

Experience: 2+ years as a Data Engineer working on data platforms, ideally in product-led or high-growth environments.

Technical Skills:

Experience building and operating ETL/ELT pipelines.

Hands-on experience with modern data stacks — our tech includes Python, SQL, Snowflake, Apache Iceberg, AWS S3, PostgresDB, Airflow, dbt, and Apache Spark, deployed via AWS, Docker, and Terraform (experience with some of these or similar technologies is expected).

Collaboration: Ability to work effectively with teammates and stakeholders.

Problem-Solving: Pragmatic approach to balancing quality with delivery needs.

Growth Mindset: Eagerness to learn, take feedback, and grow your technical skills.

You work AI-first. You will use AI daily here, and we mean daily. You do not need to arrive an expert, but you do need to arrive curious, experiment fast, and take ownership of getting good quickly. People who wait to be trained will find this uncomfortable.

Nice to Have:

Familiarity with Data Mesh or Lakehouse architectures.

Familiarity with Kubernetes, Docker, and real-time streaming technologies (e.g. Kafka, Kinesis).

Exposure to ML engineering pipelines or MLOps frameworks.

At Zego, we operate like a high-performing sports team. We come together as one, driven by ambition, backed by real support, and united by a shared mission to win.

Working here means having the backing, the coaching, and an inclusive community of Zegons across the UK and Europe who celebrate your wins and raise your game. It also means holding ourselves to high standards: moving with urgency, taking complete ownership, and embracing honest feedback that helps us continuously learn and evolve.

This is your opportunity for a career-defining experience, and one you will own.

Do not take our word for it. Read what Zegons say about us on Glassdoor .

Teams work better with time to collaborate and space to get things done. We call it Zego Hybrid: some of us are in our central London office weekly, others monthly or quarterly. It is about finding the balance between face time and focus that produces great work and a healthy life around it.

We also make a point of getting everyone in the same room. Teams come together every quarter, and once a year the whole company does, properly. It is a serious investment and consistently one of the best parts of the year. Here is what the last one looked like .

There’s more to Zego than just a job. Check out our blog for insights, stories and more.

We were also named Best Insurance Company 2025 .

We’re an equal opportunity employer and we value diversity at our company. We do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, marital status, or disability status.