Job Description

Our broker platform is the core technology crucial to Ki's success - allowing us to evolve underwriting intelligently and unlock massive scale. We're a multi-disciplined team, bringing together expertise in software and data engineering, full stack development, platform operations, algorithm research, and data science. Our squads focus on delivering high-impact features - we favour a highly iterative, analytical approach.
You will be designing and developing complex data processing modules ranging from ETL to ML working closely with the Ki Algorithm team, who are developing the machine learning models that set key apart from the traditional syndicates. In addition, you will also work closely to the Ki Platform Team, responsible for architecting, and operating the core of the Ki platform.
We're looking for a Data Engineer to join our Development team.
What you'll be doing day to day
You will work with actuaries, data scientists and engineers to design, build, optimise and maintain production grade data pipelines to feed the Ki algorithm as well as to understand how we can make best use of new internal and external data sources. You will also design and engineer a data model which can support our ambitions for growth and scale, create frameworks, infrastructure and systems to manage and govern Ki's data asset and work with the broader Engineering community to develop our data and MLOps capability infrastructure.
Your experience
You should have experience designing data models and developing industrialised data pipelines, a strong knowledge of database and data lake systems and be comfortable writing Python and SQL (any flavour). be smilarly comfortable with shell scripting with Bash or similar, provisioning new infrastructure in a leading cloud provider, preferably GCP (or AWS) and have experience creating DataOps pipelines. Finally you will be adept at working in an Agile environment, actively participating in approaches such as Scrum or Kanban.
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Role tech stack
Python


SQL


Bash


AWS


Google Cloud Platform


Kanban