Data engineer in insurance

Look at the latest headlines and you will see something Ki insures. Think space shuttles, world tours, wind farms, and even footballers' legs. Ki's mission is simple. Digitally disrupt and revolutionise a 335-year-old market. Working with Google and UCL, Ki has created a platform that uses algorithms, machine learning and large language models to give insurance brokers quotes in seconds, rather than days. Ki is proudly the biggest global algorithmic insurance carrier. It is the fastest growing syndicate in the Lloyd's of London market, and the first ever to make $100m in profit in 3 years.

• Work with actuaries, data scientists and engineers to design, build, optimise and maintain production grade data pipelines to feed the Ki algorithm
• Work with actuaries, data scientists and engineers to understand how we can make best use of new internal and external data sources
• Work with data architects to design and engineer data models and supporting infrastructure which can support our ambitions for growth and scale
• Create frameworks, infrastructure and systems to manage and govern Ki's data asset
• Work with the broader Engineering community to develop our data and MLOps capability infrastructure

• Strong experience in software engineering with proficiency in a language such as Python for API development, data engineering and automation tasks
• A background in working with storage solutions such as PostgreSQL, MySQL, and BigQuery
• Experience in API development using tools such as FastAPI or Flask, enabling data access and integration across systems
• Hands-on experience with dbt or Dataform, building modular, tested data models
• Experience orchestrating data pipelines with a modern orchestrator such as Dagster, Airflow, or Prefect
• Solid knowledge of cloud platforms (GCP and/or AWS), with the ability to design and deploy data solutions at scale
• Experience with IAC and CI/CD pipelines to ensure reliable, repeatable, and automated deployments
• An understanding of data modelling, ETL/ELT processes, and best practices for data quality and governance
• Collaborative mindset, with the ability to work closely with stakeholders such as Exposure Management, Portfolio Management, and Data Science
• Curiosity, adaptability, and enthusiasm for working in an agile, squad-based environment
• Будет плюсом: Experience working with large, complex, and siloed data estates, with a track record of simplifying and streamlining processes; A foundation in system design, with the ability to architect scalable, maintainable, and resilient data systems

• You'll get a highly competitive remuneration and benefits package. This is kept under constant review to make sure it stays relevant.
• We understand the power of saying thank you and take time to acknowledge and reward extraordinary effort by teams or individuals.