At a Glance
- Tasks: Own and scale data infrastructure for cutting-edge AI processing of financial documents.
- Company: Exciting early-stage start-up with massive growth potential in the financial sector.
- Benefits: Competitive salary up to £120k, profit share, and remote work flexibility.
- Other info: VISA sponsorship available; thrive in a dynamic, fast-paced environment.
- Why this job: Join a small team making a real impact with AI in finance and own your projects.
- Qualifications: 3-6 years experience in data pipelines, Python, and ML systems.
The predicted salary is between 100000 - 120000 € per year.
Salary: up to ~£120k + profit share
Location: Old Street (4-5 office days/week)
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.
Cash in your account on a regular basis - not a promise of a huge payout IF the company succeeds and sells.
You'll own data infrastructure end-to-end - ingestion, transformation, storage, delivery. 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.
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: 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.)
- Web scraping and document parsing at scale
VISA sponsorship is available if needed (but you need to be already living in the UK).
Data Engineer - Python & AWS - Fully Remote in City of London employer: Wave Group
Join a pioneering start-up at the forefront of financial data processing, where your contributions directly impact the success of our innovative AI-driven solutions. With a competitive salary and profit-sharing model, we foster a dynamic work culture that values collaboration and creativity, offering you the chance to grow alongside a small, dedicated team. Embrace the opportunity to take ownership of cutting-edge data infrastructure in a fully remote role, while enjoying the benefits of working in a vibrant location like Old Street, known for its thriving tech community.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer - Python & AWS - Fully Remote in City of London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, especially those who work at companies you're interested in. A friendly chat can open doors and give you insights that job descriptions just can't.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your data engineering projects. This is your chance to demonstrate your Python and AWS prowess, so make it shine!
✨Tip Number 3
Prepare for interviews by practising common questions and scenarios related to data pipelines and ML systems. We all know that confidence is key, so the more you rehearse, the better you'll perform!
✨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 take that extra step to connect with us directly.
We think you need these skills to ace Data Engineer - Python & AWS - Fully Remote in City of London
Some tips for your application 🫡
Show Your Passion for Data:When writing your application, let us see your enthusiasm for data engineering! Share specific examples of projects you've worked on that highlight your skills in Python and AWS. We love to see how you’ve tackled challenges and made an impact in your previous roles.
Tailor Your CV:Make sure your CV is tailored to the job description. Highlight your experience with data pipelines and ML systems, and don’t forget to mention any AI-native projects you've been involved in. We want to see how your background aligns with our needs!
Be Clear and Concise:Keep your application clear and to the point. Use bullet points where possible to make it easy for us to read through your experience and skills. We appreciate a well-structured application that gets straight to the good stuff!
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 to join our team at StudySmarter!
How to prepare for a job interview at Wave Group
✨Know Your Tech Inside Out
Make sure you’re well-versed in Python, AWS, and any other tech mentioned in the job description. Brush up on your experience with data pipelines and ML systems, as you'll likely be asked to discuss specific projects you've worked on.
✨Showcase Your Problem-Solving Skills
Prepare examples of how you've tackled data quality issues or improved data processing in previous roles. This is a small team, so they’ll want to see that you can think on your feet and come up with solutions without a safety net.
✨Understand the Business Context
Familiarise yourself with the financial industry and the importance of clean, structured datasets. Being able to discuss how your work impacts decision-making for clients will show that you understand the bigger picture.
✨Ask Insightful Questions
Prepare thoughtful questions about their data infrastructure and future plans. This not only shows your interest in the role but also gives you a chance to assess if the company aligns with your career goals.
