data engineer for AI-driven insurance

Zego builds real-time, AI-driven infrastructure for innovative insurance, providing good drivers with coverage that reflects how they actually drive.

• Act as a strong technical contributor within the Data Engineering team;
• Support less experienced engineers through pairing, code reviews, and knowledge sharing;
• Promote data engineering best practices, including testing, CI/CD, observability, and infrastructure as code;
• Design, build, and maintain scalable and secure data pipelines, warehouses, and streaming systems;
• Ensure data is modelled and structured for analytics, data science, and operational use cases;
• Contribute to the evolution of the data architecture to support current and future business needs;
• Partner with teams across the business to understand requirements and translate them into robust technical solutions;
• Identify opportunities for optimisation, re-architecture, or tool improvements;
• Contribute to the delivery of the technical roadmap for data engineering.

• 4+ Years of experience as a Data Engineer working on scalable data platforms, ideally in product-led or high-growth environments;
• Solid experience designing, building, and operating ETL/ELT pipelines and large-scale data architectures;
• Hands-on experience with modern data stacks; experience with technologies similar to Python, SQL, Snowflake, Apache Iceberg, AWS S3, PostgresDB, Airflow, dbt, Apache Spark, AWS, Docker, and Terraform is essential;
• Ability to work effectively with cross-functional stakeholders and translate technical concepts into business value;
• Experience supporting other engineers through code reviews, pairing, and knowledge sharing;
• Pragmatic approach to balancing technical excellence with delivery needs;
• Curiosity, fast experimentation, and ownership of developing AI skills; candidates who wait to be trained may find the role uncomfortable;
• Nice to have: Experience building Data Mesh or Lakehouse architectures, familiarity with Kubernetes, Docker, and real-time streaming technologies such as Kafka or Kinesis, exposure to ML engineering pipelines or MLOps frameworks.

• Bonus and share options;
• Private medical insurance;
• Pension and generous holiday;
• £1,000 A year to spend on getting to the office or learning something new;
• Teams meet in person quarterly, and the whole company meets once a year;
• No conditions specified.