Description
We are looking for a Lead Data Engineer to be a technical authority within our data engineering function, helping to shape and evolve our data platform to meet Zego's ambitious growth.
This is a hands-on technical role - you'll design and build scalable, resilient, and secure data systems, while acting as a mentor and technical guide for other engineers. You'll work closely with peers across Engineering, Data Science, Analytics, and Product to ensure our data infrastructure is efficient, reliable, and future-proof.
While this role doesn't involve direct line management, you'll be expected to foster technical excellence within the team by sharing knowledge, reviewing code, and coaching others.
Responsibilities:
- Act as a senior technical reference point for the Data Engineering team.
- Mentor and coach other engineers, helping them grow their technical skills and deliver high-quality work.
- 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 definition and execution of the technical roadmap for data engineering.
Requirements:
- 5+ years as a Data Engineer working on scalable data platforms, ideally in product-led or high-growth environments.
- Proven expertise in designing, building, and operating ETL/ELT pipelines and large-scale 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.
- Ability to work effectively with cross-functional stakeholders, translating technical concepts into business value.
- Demonstrated experience in supporting the growth of other engineers through code reviews, pairing, and knowledge sharing.
- Pragmatic approach to balancing technical excellence with delivery needs.
Nice to Have:
- 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.
Benefits:
- Market-competitive salary, benchmarked against your function and reviewed every year.
- Annual performance bonus, linked to company performance and your contribution.
- Share options, a real stake in the company and a share in the future you build.
- Private medical insurance.
- Pension, generous holiday, and £1,000 a year to spend on getting to the office or learning something new.
- Cutting-edge systems and tools, so you always have what you need to drive your impact.