Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Data Engineer

Innovation Group
City of London
2 days ago
Create job alert

Please visit our careers site to find out more about working at Ki


Job Details: Data Engineer
Role Details Who are we?đź‘‹

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 be joining the Portfolio Data squad to tackle some of the most critical challenges in transforming how we manage insurance exposure and risk aggregations.


The current data estate is siloed, inefficient, and difficult to scale, so the team is designing a robust solution to support seamless data ingestion, scalable storage, and optimise for downstream use cases, including developing our algorithmic underwriting capability.


Alongside this, the squad is building a trusted source of truth for insurance exposure data — aligning methodologies with the Exposure Management team and delivering intuitive, high-value views and dashboards for Portfolio Management.


By joining us, you’ll help modernize our data landscape, eliminate manual and unreliable processes, and empower the business with faster, smarter, and more reliable insights.


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
  • 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
  • Work with the broader Engineering community to develop our data and MLOps capability infrastructure

What you will bring to the role

  • 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
  • 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.
  • 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

Ki Values

  • Know Your Customer: Put yourself in their shoes. Understand and balance the different needs of our customers, acting with integrity and empathy to create something excellent
  • Grow Together: Empower each other to succeed. Recognise the work of our teams, while celebrating individual success. Embrace diverse perspectives so we can develop and grow together.
  • Be Courageous: Think big, push boundaries. Don’t be afraid to fail because that’s how we learn. Test, adapt, improve - always strive to be better.

Our culture

At Ki, we are committed to creating an inclusive environment where every colleague is valued and respected for who they are and can do the best work of their careers.


Inclusion is a critical foundation of our business and people strategies and supports our vision of becoming a market-leading, digital and data-led specialty insurance business. An inclusive workplace fuels innovation because creativity thrives when everyone feels valued, respected, and supported to drive it. So, no matter who you are, where you’re from, how you think, or who you love, we believe you should be you.


What we offer

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.


If this sounds like a role and a culture that appeals to you, apply now!


#J-18808-Ljbffr

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.