Posted anonymously by pipeline_eng
Hi everyone, I’m a data engineer currently based in London and I’m exploring a startup idea that I’m planning to use for an application under the UK Innovator Founder visa. I’d really appreciate honest feedback from people in fintech, data engineering, or SaaS.
The concept
A SaaS platform that automates financial data pipelines and analytics infrastructure for fintech startups and small hedge funds. From my experience working with financial data, a lot of smaller firms struggle with setting up and maintaining ETL pipelines, connecting to market data APIs, and building analytics dashboards. They often don’t have dedicated data engineering teams, so infrastructure becomes a bottleneck.
What the platform would do:
- Connect to financial APIs (market data, macro datasets, etc.)
- Automatically generate ETL pipelines for ingestion and transformation
- Store structured datasets in cloud data warehouses
- Provide built-in analytics dashboards
- Monitor pipelines and send alerts for failures or anomalies
The goal is to reduce the time required to set up financial data infrastructure by around 70–80%.
Target users and model
Fintech startups, small hedge funds and investment research firms. SaaS subscription: Starter £49/month, Pro £199/month, Enterprise £999/month.
My background
Master’s in Data Science, currently working in data engineering building ETL pipelines and data infrastructure in the finance space. Python, PySpark, cloud storage and orchestration tools. Planning an MVP with a Python/FastAPI backend, automated pipeline generation and a simple dashboard interface.
Questions
- Is this problem actually painful enough that fintech companies would pay for it?
- Are there already tools doing this well that I should study?
- What features would make this genuinely useful for early-stage fintech teams?
- Would you recommend narrowing the niche further (e.g. crypto, trading firms, research)?
I’d love any feedback, positive or critical, before I invest serious time building the MVP. Thanks!
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