At a Glance
- Tasks: Build and scale data pipelines for transforming unstructured financial data into clean datasets.
- Company: Early-stage AI company revolutionising financial data infrastructure.
- Benefits: Competitive salary, equity, and opportunities for professional growth.
- Other info: High ownership from day one with potential for leadership roles.
- Why this job: Join a small, technical team and make a real impact in the fintech space.
- Qualifications: Experience with data pipelines, strong Python skills, and exposure to AI workflows.
The predicted salary is between 60000 - 80000 £ per year.
We're working with an early-stage AI company building infrastructure that transforms large volumes of unstructured financial data into clean, queryable datasets used by major financial institutions. They're looking for a Data Infrastructure Engineer to own data pipelines end-to-end — from ingestion and transformation through to delivery — while working closely with AI agents and LLM-powered workflows.
What you'll be doing
- Building and scaling production-grade data pipelines handling large volumes of messy, unstructured data
- Designing ingestion, transformation, storage, and delivery systems end-to-end
- Working with AI agents and LLM workflows for document extraction and data processing
- Improving reliability, observability, and data quality across the platform
- Helping shape the architecture of an AI-native data platform from an early stage
What they're looking for
- Experience building and owning production data pipelines
- Strong Python engineering skills
- Experience working with unstructured data at scale
- Exposure to AI agents, LLMs, or orchestration workflows in production
- Background in fintech, market data, or similar high-trust environments is a plus
- Engineers who care deeply about data quality and correctness
Tech
- Python
- Async systems / queues / web scraping
- Postgres / SQLite
- AI agents & LLM workflows
- Data pipelines & infrastructure
Why it's interesting
- High ownership from day one
- Strong mix of AI infrastructure + data engineering
- Real-world financial datasets with meaningful complexity
- Small, highly technical team with strong traction
- Opportunity to grow into a broader platform / technical leadership role
Data Engineer - Infra in London employer: Atarus
Join a pioneering AI company at the forefront of transforming financial data, where you'll have the opportunity to take ownership of data pipelines from inception to delivery. With a competitive salary and equity options, our collaborative work culture fosters innovation and personal growth, allowing you to shape the architecture of an AI-native data platform while working alongside a highly skilled team. This role not only offers the chance to work with complex datasets but also provides a pathway to advance into technical leadership as the company scales.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer - Infra in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, especially those working with AI and data engineering. A friendly chat can lead to opportunities that aren’t even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving data pipelines and unstructured data. This will give potential employers a taste of what you can do.
✨Tip Number 3
Prepare for technical interviews by brushing up on Python and data pipeline concepts. Practice coding challenges and be ready to discuss your past experiences with data quality and reliability.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for passionate Data Engineers who want to make an impact in the AI space. Your next big opportunity could be just a click away!
We think you need these skills to ace Data Engineer - Infra in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that match the job description. Highlight your experience with data pipelines, Python, and any work with unstructured data to catch our eye!
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about data engineering and how your background aligns with our mission. Share specific examples of your past projects that relate to building and scaling data pipelines.
Showcase Your Technical Skills:Don’t shy away from listing your technical skills! Mention your experience with async systems, Postgres, and any AI agents or LLM workflows you've worked with. We love seeing how you’ve tackled challenges in your previous roles.
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!
How to prepare for a job interview at Atarus
✨Know Your Data Pipelines
Make sure you can talk confidently about your experience with data pipelines. Be ready to discuss specific projects where you've built or owned production-grade data pipelines, especially those handling unstructured data. Highlight any challenges you faced and how you overcame them.
✨Showcase Your Python Skills
Since strong Python engineering skills are a must, brush up on your Python knowledge before the interview. Prepare to discuss your coding practices, libraries you frequently use, and any relevant projects. You might even be asked to solve a coding problem, so practice some common algorithms and data structures.
✨Familiarise Yourself with AI and LLMs
Given the focus on AI agents and LLM workflows, it’s crucial to understand how these technologies work. Be prepared to explain how you've used AI in your previous roles, particularly in relation to data processing and document extraction. This will show that you’re not just a data engineer but also someone who can leverage cutting-edge technology.
✨Emphasise Data Quality and Reliability
The company values engineers who care about data quality and correctness. Be ready to discuss your approach to ensuring data reliability and observability in your projects. Share examples of how you've improved data quality in past roles, as this will resonate well with their expectations.
