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 hybrid work model.
- Other info: High ownership from day one with opportunities for career growth.
- Why this job: Join a small, technical team and shape the future of AI-native data platforms.
- Qualifications: Experience in data pipelines, strong Python skills, and exposure to AI workflows.
The predicted salary is between 80000 - 100000 £ 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 - Python / Async / AI in London employer: Atarus
Join an innovative early-stage AI company that is revolutionising the financial data landscape. With a hybrid work model, competitive salary, and equity options, you'll enjoy a collaborative culture that prioritises high ownership and technical excellence. This role offers significant opportunities for personal and professional growth as you shape the architecture of a cutting-edge AI-native data platform alongside a small, highly skilled team.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer - Python / Async / AI in London
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We think you need these skills to ace Data Engineer - Python / Async / AI in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Atarus, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Atarus. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Atarus
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Atarus!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
