Description
We are looking for a Data Engineer to join our data engineering function, helping to build and maintain the data platform that powers Zego's ambitious growth.
This is a hands-on technical role. You will build and maintain scalable, reliable, and secure data pipelines, working within an established platform and architecture. You will work closely with peers across Engineering, Data Science, Analytics, and Product to ensure our data infrastructure is efficient and reliable.
Responsibilities
- 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.
- 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.
- 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.
Requirements
- 2+ years as a Data Engineer working on data platforms, ideally in product-led or high-growth environments.
- 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.
- Ability to work effectively with teammates and stakeholders.
- Pragmatic approach to balancing quality with delivery needs.
- 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.
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.
Benefits
- Market-competitive salary, benchmarked against your function and reviewed every year.
- Annual performance bonus, linked to company performance and your contribution.
- Share options, a real stake in the company and a share in the future you build.
- Private medical insurance.
- Pension, generous holiday, and £1,000 a year to spend on getting to the office or learning something new.
- Cutting-edge systems and tools, so you always have what you need to drive your impact.