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- Tasks: Design and build data pipelines to unify fragmented investment data for a fintech platform.
- Company: Dynamic London fintech tackling data challenges in private markets.
- Benefits: Competitive salary, bonuses, hybrid work, and opportunities for professional growth.
- Other info: Collaborative culture with a focus on clean engineering and innovative solutions.
- Why this job: Make a real impact by solving complex data problems and shaping AI integration.
- Qualifications: Strong Python skills and experience with data engineering; no prior private markets experience needed.
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 City of 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 City of 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. This is your chance to demonstrate your problem-solving abilities and how you tackle messy data challenges.
✨Tip Number 3
Prepare for technical interviews by brushing up on your knowledge of data models and architectures. Be ready to discuss how you would approach real-world data problems, as this will show your strong engineering judgement and practical experience.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive and engaged with our platform. Let’s get you that dream job!
We think you need these skills to ace Data Engineer - Investment Platform in City of 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 engaging and relevant!
Showcase Your Projects: If you've worked on any relevant projects, whether personal or professional, make sure to include them. We love seeing real-world applications of your skills, especially if they involve data ingestion, reconciliation, or AI. It gives us a taste of what you can bring to the table!
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’re considered for the role. Plus, it shows us you’re keen on joining our team at StudySmarter!
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 to feel confident.
✨Understand the Fintech Landscape
Familiarise yourself with the current trends in fintech, particularly around private markets and investment data. Being able to discuss how different fund administrators report data and the challenges they face will impress your interviewers and show your genuine interest in the sector.
✨Emphasise Collaboration and Communication
This role isn't just about data; it's about working across teams. Be ready to talk about how you've collaborated with others in previous roles, especially in cross-functional settings. Highlight your ability to communicate complex technical concepts to non-technical stakeholders, as this will be crucial in a hybrid environment.