Kalpa Group · London, United Kingdom ·
Build and maintain scalable data pipelines and models to unify fragmented private-market investment data into a clean, real-time platform using Python, Azure, and Delta Lake.
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Zello · Austin, Texas
Stakemate · London, England, United Kingdom
ApartmentIQ · Remote
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, 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:
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.
incident · London