At a Glance
- Tasks: Own and scale data infrastructure for cutting-edge AI processing.
- Company: Exciting early-stage start-up transforming financial data with AI.
- Benefits: Competitive salary, profit share, equity, and a clear progression path.
- Other info: Join a small, dynamic team with immense growth potential.
- Why this job: Be a key player in a high-impact team driving innovation in finance.
- Qualifications: 3-6 years in data pipelines, AI experience, and a passion for quality.
The predicted salary is between 100000 - 120000 € per year.
This early stage start-up is processing hundreds of thousands of unstructured financial documents into clean, structured datasets for some of the world's largest financial institutions - producing the output of 50, with a team of 5. Forecasting £1.5m revenue within their first 12 months, they have immense potential - not based on hype or inflated valuations, but rather achieving mega productivity through intelligent application of AI agents. Their mid-term goal is ~£50m revenue with a sub-30 person team.
What's in it for you?
- Profit share. Cash in your account on a regular basis - not a promise of a huge payout IF the company succeeds and sells.
About the role
You'll own data infrastructure end-to-end - ingestion, transformation, storage, delivery. The current agent system works. Your job is to scale it: more documents, more asset classes, more clients. You'll make architecture decisions, push the boundaries of agentic tooling and ship data as a product to institutional buyers who rely on it for decisions that move millions. Full ownership from day one, with a genuine progression trajectory for the right person. This is a predominantly backend/infra DE role. No dashboards, no stakeholder reporting - just the engine that powers everything. You'll be the most senior person in your domain from day one, with no one above you in this function - and a clear path to leadership later down the line.
Must have requirements:
- Roughly 3-6 years experience building performant data pipelines and ML systems in production at a company where data is the core product, not a support function.
- Genuinely AI-native experience: Claude Code, Cursor or equivalent as your daily environment - not occasional experiments.
- Python, Postgres, async, queues - fluent and battle-tested.
- Experience in a small team (2-30 people) where you owned the whole function without a safety net.
- Data quality obsessive - inconsistencies bother you until they're fixed, not flagged and forgotten.
Bonus points for:
- Experience at a financial data provider (Bloomberg, Refinitiv, Preqin, FactSet etc.) or quant finance.
- Experience with LLMs in production / agentic workflow design.
- Web scraping and document parsing at scale.
- Staff/Principal-level IC ownership at a larger org.
VISA sponsorship is available if needed (but you need to be already living in the UK).
Founding Data Engineer employer: Wave Group
As a Founding Data Engineer at this innovative start-up in Old Street, you'll be part of a dynamic team dedicated to transforming unstructured financial data into actionable insights for major financial institutions. With a strong focus on employee growth and profit sharing, you will enjoy full ownership of your work from day one, alongside a clear path to leadership in a collaborative and fast-paced environment that values creativity and initiative.
StudySmarter Expert Advice🤫
We think this is how you could land Founding Data Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the industry, especially those who work at start-ups or in data engineering roles. Use platforms like LinkedIn to connect and engage with them; you never know who might have a lead on your dream job!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving data pipelines and AI systems. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Be proactive! Don’t just wait for job postings to appear. Reach out directly to companies you admire, like us at StudySmarter, and express your interest. Sometimes, the best opportunities come from a simple conversation.
✨Tip Number 4
Prepare for interviews by practising common technical questions and scenarios related to data engineering. We recommend doing mock interviews with friends or using online platforms to get comfortable with the process before you step into the real deal.
We think you need these skills to ace Founding Data Engineer
Some tips for your application 🫡
Show Your Passion for Data:When you're writing your application, let your enthusiasm for data engineering shine through. We want to see that you’re not just looking for a job, but that you genuinely care about building robust data pipelines and making an impact in the financial sector.
Tailor Your Experience:Make sure to highlight your relevant experience in data engineering, especially any work with AI-native tools or in small teams. We love seeing how your background aligns with our needs, so don’t hold back on those specific projects that showcase your skills!
Be Clear and Concise:Keep your application straightforward and to the point. We appreciate clarity, so avoid jargon and fluff. Focus on what makes you a great fit for the role and how you can contribute to our mission of transforming financial data processing.
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 this exciting opportunity. Plus, it shows you’re keen on joining our team!
How to prepare for a job interview at Wave Group
✨Know Your Data Inside Out
Make sure you’re well-versed in the specifics of data pipelines and ML systems. Brush up on your experience with Python, Postgres, and async programming. Be ready to discuss how you've built performant data solutions in the past, as this role demands a deep understanding of data as a core product.
✨Showcase Your AI-Native Experience
Since the company is looking for someone with genuine AI-native experience, prepare examples of how you've used tools like Claude Code or Cursor in your daily work. Highlight any projects where you pushed the boundaries of agentic tooling, as this will resonate well with their focus on intelligent applications.
✨Demonstrate Ownership and Leadership
This role offers full ownership from day one, so be ready to talk about times when you took charge of a project or function. Share specific instances where you made architecture decisions and how you handled challenges without a safety net. This will show that you're the right fit for a senior position.
✨Be a Data Quality Advocate
The company values a data quality obsessive, so come prepared to discuss how you ensure data integrity. Talk about your approach to fixing inconsistencies and maintaining high standards in your work. This will demonstrate your alignment with their commitment to delivering clean, structured datasets.
