Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Build and optimise data pipelines for a cutting-edge investment platform.
- Company: Innovative fintech tackling data challenges in private markets.
- Benefits: Competitive salary, bonuses, hybrid work, and great perks.
- Other info: Dynamic team culture with opportunities for growth and innovation.
- Why this job: Make a real impact by solving complex data problems with AI integration.
- Qualifications: Strong Python skills and a passion for clean engineering.
The predicted salary is between 90000 - 140000 £ per year.
There's a quiet data problem at the heart of private markets and a London fintech is building the infrastructure to solve it.
Every PE fund, credit strategy and infrastructure vehicle reports NAVs, capital calls and investor positions differently. Different fund admins. Different formats. Different cadences. Some APIs are terrible. Many don't exist at all.
Now imagine giving a wealth manager in Zurich, a private bank in Hong Kong and an adviser in Sydney one clean, regulator-ready view across all of it - in near real time. That's the challenge that we need a software & data engineer to solve.
You'll own ingestion pipelines from global fund administrators, design scalable data models across relational, NoSQL and Delta architectures, and solve the reconciliation problems that turn fragmented investment data into a clean, unified layer the platform can rely on.
You'll also help shape how AI gets embedded into the platform - not as a side experiment, but as core infrastructure. The engineering environment is modern and evolving fast. You get room to shape systems, not just maintain them.
Python and Azure underpin a modern distributed data platform built around ingestion pipelines, lakehouse architecture, streaming workflows, Databricks, CI/CD, observability and product-facing data services. The team is actively pushing further into AI-native workflows - using LLMs to improve ingestion, reconciliation and reporting, while investing heavily in data quality, lineage and platform reliability.
The wider application stack is React, TypeScript and Node, so this isn't a siloed data engineer role hidden away from the product. Engineers here work across the platform where it makes sense: infrastructure, pipelines, APIs, application architecture and the data experiences exposed to end users.
Engineering culture matters too. Leadership comes from deep trading-tech and asset management infrastructure backgrounds. Pragmatic people. Low ego. Strong technical standards. The kind of environment where good architectural pushback is welcomed.
You'll enjoy logging every morning if:
- Care deeply about data quality and clean engineering
- Enjoy solving messy, high-consequence data problems
- Like operating across data, infrastructure and application engineering
- Want ownership, not endless Jira ticket throughput
- Are curious about fintech, wealth or private markets
- See AI as an opportunity to build better systems, not just automate tasks
You don't need prior private markets experience. What matters is strong engineering judgement, deep Python capability and the ability to navigate messy, real-world data problems without overengineering them.
Curious to know more? Hit apply or send me a DM — informal first conversations are taking place now.
Data Engineer - Investment Platform in London employer: Kalpa Group
Contact Detail:
Kalpa Group Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineer - Investment Platform in London
✨Tip Number 1
Network like a pro! Reach out to people in the fintech space, especially those working with data engineering. Use LinkedIn to connect and don’t be shy about asking for informational chats. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving Python and data pipelines. Share it during interviews or even on your LinkedIn profile. It’s a great way to demonstrate your capabilities beyond just words.
✨Tip Number 3
Prepare for technical interviews by brushing up on your problem-solving skills. Practice coding challenges that focus on data structures and algorithms. This will help you feel confident when tackling those tricky questions that come up in interviews.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals who are passionate about data and fintech. Plus, applying directly can sometimes give you a better chance of getting noticed by hiring managers.
We think you need these skills to ace Data Engineer - Investment Platform in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the Data Engineer role. Highlight your Python expertise, experience with data pipelines, and any work with Azure or AI technologies. We want to see how you can tackle messy data problems!
Craft a Compelling Cover Letter: Your cover letter is your chance to show us your personality and passion for fintech. Share why you're excited about solving data challenges in private markets and how your background makes you a great fit for our team. Keep it informal but professional!
Showcase Relevant Projects: If you've worked on projects involving data ingestion, reconciliation, or AI, make sure to mention them! We love seeing real-world examples of how you've tackled complex data issues. This helps us understand your hands-on experience and problem-solving skills.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates. Plus, we love seeing candidates who take that extra step!
How to prepare for a job interview at Kalpa Group
✨Know Your Data Inside Out
Make sure you understand the intricacies of data engineering, especially in relation to investment platforms. Brush up on your knowledge of ingestion pipelines, data models, and how to handle messy data. Being able to discuss specific challenges and solutions you've encountered will show your expertise.
✨Showcase Your Python Skills
Since Python is a key part of the role, be prepared to demonstrate your coding skills. You might be asked to solve a problem on the spot or discuss past projects where you used Python to tackle data issues. Practice coding challenges related to data manipulation and processing.
✨Familiarise Yourself with AI Applications
The company is keen on embedding AI into their platform, so it’s crucial to understand how AI can enhance data processes. Be ready to discuss any experience you have with AI, particularly in relation to data quality and automation. Showing enthusiasm for AI as a tool for improvement will resonate well.
✨Emphasise Collaboration and Communication
This role isn’t just about data; it’s about working across teams. Highlight your experience in collaborative environments and how you’ve effectively communicated technical concepts to non-technical stakeholders. This will demonstrate that you’re not just a coder but a team player who can bridge gaps.