Data Engineer

Job description

At Zego, we're on a mission to do the good thing, not the insurance thing.

Insurance hasn't changed much in over a century. The way we live, work and travel has. We're building the real-time, AI-driven infrastructure that powers innovative insurance, so good drivers get cover that works the way they actually drive.

For us, AI isn't a line on a roadmap or a buzzword on a slide. It's our operating reality, and it's how we build products that back drivers instead of the old insurance playbook.

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

• 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.
• 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.