Data Engineer

Innovation Group
City of London
5 months 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.

New Data Engineering Employers to Watch in 2026: UK and Global Companies Driving the Data Revolution

Data engineering is at the heart of the digital economy, transforming raw data into actionable insights, powering analytics, AI systems, and cloud infrastructure. As the UK and global markets continue to invest heavily in data platforms, pipelines, and real-time analytics, demand for skilled data engineers is growing rapidly. For professionals exploring opportunities on www.DataEngineeringJobs.co.uk , the critical question is: which companies are expanding, hiring, and shaping the future of data-driven business? This article highlights new data engineering employers to watch in 2026, including UK startups, scale-ups, and international firms expanding in the UK.

How Many Data Engineering Tools Do You Need to Know to Get a Data Engineering Job?

If you’re aiming for a career in data engineering, it can feel like you’re staring at a never-ending list of tools and technologies — SQL, Python, Spark, Kafka, Airflow, dbt, Snowflake, Redshift, Terraform, Kubernetes, and the list goes on. Scroll job boards and LinkedIn, and it’s easy to conclude that unless you have experience with every modern tool in the data stack, you won’t even get a callback. Here’s the honest truth most data engineering hiring managers will quietly agree with: 👉 They don’t hire you because you know every tool — they hire you because you can solve real data problems with the tools you know. Tools matter. But only in service of outcomes. Jobs are won by candidates who know why a technology is used, when to use it, and how to explain their decisions. So how many data engineering tools do you actually need to know to get a job? For most job seekers, the answer is far fewer than you think — but you do need them in the right combination and order. This article breaks down what employers really expect, which tools are core, which are role-specific, and how to focus your learning so you look capable and employable rather than overwhelmed.

What Hiring Managers Look for First in Data Engineering Job Applications (UK Guide)

If you’re applying for data engineering jobs in the UK, the first thing to understand is this: Hiring managers don’t read every word of your CV. They scan it. They look for signals of relevance, credibility, delivery and collaboration — and if they don’t see the right signals quickly, your application may never get a second look. In data engineering, hiring managers are especially focused on whether you can build and operate reliable, scalable data systems, handle real-world data challenges and work effectively with analytics, BI, data science and engineering teams. This guide breaks down exactly what they look at first in your application — and how to shape your CV, portfolio and cover letter so you stand out.