Location
London

Hours
Full Time

Salary
Competitive salary

About the Role
At Zego, we are transforming insurance by leveraging AI to create smarter, faster, and more efficient solutions. As a Senior Data Engineer, you will be a key technical contributor within our data engineering team, responsible for designing and building scalable, resilient, and secure data systems that support Zego’s ambitious growth. This hands-on role involves close collaboration with Engineering, Data Science, Analytics, and Product teams to ensure our data infrastructure is efficient, reliable, and future-proof. You will also support the team by sharing knowledge, reviewing code, and promoting technical excellence. While this role does not include line management responsibilities, it offers significant opportunity to influence the technical direction and quality of our data platform.

Experience
- 4+ years as a Data Engineer working on scalable data platforms, ideally in product-led or high-growth environments.
- Proven experience designing, building, and operating ETL/ELT pipelines and large-scale data architectures.
- Hands-on experience with modern data stacks including Python, SQL, Snowflake, Apache Iceberg, AWS S3, PostgresDB, Airflow, dbt, and Apache Spark.
- Familiarity with deployment technologies such as AWS, Docker, and Terraform (or similar).
- Experience supporting other engineers through code reviews, pairing, and knowledge sharing.
- Ability to work effectively with cross-functional stakeholders, translating technical concepts into business value.
- Pragmatic approach balancing technical excellence with delivery needs.
- Comfortable working in an AI-first environment, eager to experiment and learn quickly.

About you
You are a curious and proactive engineer who embraces AI as a daily tool. You thrive in fast-moving environments and take ownership of your growth and the quality of your work. You enjoy collaborating across teams and mentoring others to raise the bar for technical excellence. You are passionate about building robust, scalable data platforms that drive business impact.

Qualifications
While formal qualifications are not specified, strong hands-on experience with the technologies and practices listed above is essential. Experience or interest in the following is a plus:
- 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.