
Head of Data Engineering
- London
- Permanent
- Full-time
- Lead, mentor, and develop a high performing team of Data Engineers, fostering a culture of learning, excellence, and ownership.
- Define and execute the data engineering roadmap aligned with Zego’s business goals.
- Collaborate closely with leaders in Engineering, Analytics, Product and Insurance Pricing to ensure alignment and prioritisation.
- Own the end-to-end architecture and reliability of Zego’s data platform, from ingestion and transformation to governance and observability.
- Guide the team in evolving our ETL/ELT pipelines, data models, and real-time processing frameworks.
- Lead the development of new data architectures and the introduction of new technologies.
- Drive the adoption of modern data engineering practices including testing, CI/CD, and infrastructure as code.
- Identify opportunities for tooling improvements, tech upgrades, or re-architecting where needed.
- Leadership Experience: 2+ years leading a team of Data Engineers, either as a Technical Lead or hands-on Manager.
- Technical Excellence: 5+ years as a Data Engineer with experience designing and operating scalable data platforms in fast-paced environments such as product-led companies or high-growth startups
- Stakeholder Management: Skilled in collaborating with diverse stakeholders and translating complex technical ideas into clear, business-focused discussions.
- Proven expertise in designing, building, and operating data pipelines, warehouses, and scalable data architectures.
- Deep 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.
- Coaching & Growth Mindset: Passion for developing others through mentorship, feedback, and knowledge sharing.
- Pragmatic Problem-Solving: Comfortable balancing technical excellence with business priorities and constraints
- Experience building Data Mesh or Data Lake architectures.
- Familiarity with Kubernetes, Docker, and real-time streaming (e.g. Kafka, Kinesis).
- Exposure to ML engineering pipelines or MLOps frameworks.
We are sorry but this recruiter does not accept applications from abroad.