Senior 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

• Act as a strong technical contributor within the Data Engineering team.
• Support and help less experienced engineers grow through pairing, code review, and knowledge sharing.
• Promote best practices in data engineering, 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 to meet the needs of analytics, data science, and operational use cases.
• Contribute to the evolution of our data architecture, ensuring it can support both 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.

Requirements

• Experience: 4+ years 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 — our tech includes Python, SQL, Snowflake, Apache Iceberg, AWS S3, PostgresDB, Airflow, dbt, and Apache Spark, deployed via AWS, Docker, and Terraform (experience with similar technologies is essential).
• Ability to work effectively with cross-functional stakeholders, translating 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.
• 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.
• Experience building 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.