Data Engineer

Ki; Greater London; On-site; GBP 90,000 - 130,000; Full time

Benefits offered by this job

Competitive remuneration

Hybrid work environment

Job summary

Ki is building an agile data-enabled platform for algorithmic insurance in the UK. You will join the commercial performance insights squad to monitor real-time performance of our algorithmically underwritten business and upgrade the data foundation to unlock faster, more reliable insights across customer-facing products.

You will collaborate with actuaries, data scientists and engineers to design, build, and maintain production-grade data pipelines, data models and the supporting infrastructure

Qualifications

• Strong experience in software engineering with proficiency in Python for API development, data engineering and automation tasks.
• Background with storage solutions such as PostgreSQL, MySQL, and BigQuery.
• Experience in API development using FastAPI or Flask for data access and integration.
• Hands-on experience with dbt or Dataform for modular, tested data models.
• Experience orchestrating data pipelines with Dagster, Airflow, or Prefect.
• Solid knowledge of cloud platforms (GCP and/or AWS) for designing scalable data solutions.
• Experience with IaC and CI/CD pipelines to ensure reliable automated deployments.
• Understanding of data modelling, ETL/ELT, and data governance practices.
• Collaborative mindset to work with stakeholders such as Exposure Management, Portfolio Management, and Data Science.
• Curiosity and adaptability for agile, squad-based environment.

Responsibilities

• 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

Skills

Python

API development

Data modelling

ETL/ELT

Cloud platforms

CI/CD

Data governance

Collaborative mindset

Agile/squad-based work

Tools

PostgreSQL

MySQL

BigQuery

FastAPI

Flask

dbt

Dataform

Dagster

Airflow

Prefect

GCP

AWS

IaC

CI/CD pipelines

Job description

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.

Ki's teams have varied backgrounds and work together in an agile, cross-functional way to build the very best experience for its customers. Ki has big ambitions but needs more excellent minds to challenge the status-quo and help it reach new horizons.

Where you come in?

You’ll join our commercial performance insights squad to tackle some of our most critical challenges in monitoring the commercial performance of our algorithmically underwritten insurance business in real time.

We’re upgrading the foundation that captures algorithm decision data, moving from database logging to a versioned event stream with purpose-built views for each customer use case. This will unlock faster, more reliable insights across our customer-facing products.

With the growing adoption of our Monte Carlo simulation engine, we can understand the impact of changes to our algorithmic underwriting before they’re released, as well as stress-test changing market conditions. You’ll have the chance to dive deep into insurance domain modelling problems alongside data engineering.

You’ll work in an agile, cross-functional squad close to the people who consume what we build, helping us shape our engineering culture.

What you will be doing:

• 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

Who are we?

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.

Ki's teams have varied backgrounds and work together in an agile, cross-functional way to build the very best experience for its customers. Ki has big ambitions but needs more excellent minds to challenge the status-quo and help it reach new horizons.

Where you come in?

You’ll join our commercial performance insights squad to tackle some of our most critical challenges in monitoring the commercial performance of our algorithmically underwritten insurance business in real time.

We’re upgrading the foundation that captures algorithm decision data, moving from database logging to a versioned event stream with purpose-built views for each customer use case. This will unlock faster, more reliable insights across our customer-facing products.

With the growing adoption of our Monte Carlo simulation engine, we can understand the impact of changes to our algorithmic underwriting before they’re released, as well as stress-test changing market conditions. You’ll have the chance to dive deep into insurance domain modelling problems alongside data engineering.

You’ll work in an agile, cross-functional squad close to the people who consume what we build, helping us shape our engineering culture.

What you will be doing:

• 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

Requirements

• Strong experience in software engineering with proficiency in a language such asPythonfor API development, data engineering and automation tasks
• A background in working with storage solutions such asPostgreSQL, MySQL, and BigQuery
• Experience inAPI developmentusing tools such asFastAPIorFlask, 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 ofcloud platforms(GCP and/or AWS), with the ability to design and deploy data solutions at scale
• Experience withIACandCI/CD pipelinesto 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

Desirable Skills

• Experience working with large, complex, and siloed data estates, with a track record of simplifying and streamlining processes
• A foundation insystem design, with the ability to architect scalable, maintainable, and resilient data systems

Benefits

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.