At a Glance
- Tasks: Design and build data lakes, pipelines, and backend services for innovative AI platforms.
- Company: Join a dynamic venture firm at the forefront of technology and entrepreneurship.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborate with founders and investors in a fast-paced, innovative environment.
- Why this job: Make a real impact by building foundational systems for cutting-edge AI products.
- Qualifications: Strong backend engineering skills and experience with data architecture and pipelines.
The predicted salary is between 60000 - 80000 € per year.
We are looking for a strong Backend & Data Engineer to join our team and own the data and systems layer behind both our internal AI platforms and our portfolio companies. This is a hands‑on engineering role for someone who genuinely enjoys designing data lakes, modelling databases, building reliable ingestion pipelines and shipping production backend services. AI and LLM work is part of the job, but it sits on top of solid infrastructure. We need someone who can build that foundation: data gathering applications, ETL, storage architecture, APIs and the integration plumbing that turns messy real‑world data into something models and products can actually use.
You will work directly with founders, internal product owners and the investment team — designing systems for our own AI platform, supporting portfolio companies with serious data engineering needs, and helping evaluate the technical strength of AI‑driven startups we consider investing in.
Data Infrastructure & Architecture- Design and build the RAW Ventures data lake end-to-end — storage, partitioning, schema evolution and access patterns.
- Architect relational and analytical databases (Postgres, ClickHouse, BigQuery, DuckDB or similar).
- Own data modelling, governance, cost and reliability across all sources.
- Build large‑scale data acquisition services — APIs, scrapers, event streams and file ingestion.
- Develop and operate ETL/ELT pipelines (Airflow, Dagster, dbt, Spark or equivalent).
- Ensure robust deduplication, validation, monitoring and data‑quality tooling.
- Design and ship production backend services in Python and/or TypeScript/Go — REST APIs, workers, event‑driven components.
- Containerise and deploy via Docker/Kubernetes with CI/CD and infrastructure‑as‑code.
- Own reliability, security and operational quality, not just features.
- Build infrastructure for LLM‑based systems — RAG pipelines, vector stores, embedding and retrieval layers.
- Integrate model APIs (Anthropic, OpenAI, open‑source) into backend services and agent workflows.
- Develop or fine‑tune ML models for forecasting, NLP or recommendation and ship as stable product features.
- Support the investment team with technical due diligence on AI and data‑heavy startups.
- Assess architectures, pipelines, scalability and defensibility of underlying tech.
- Provide technical insight to inform investment decisions and portfolio strategy.
- Strong, hands‑on backend engineering experience with production systems at meaningful scale.
- Deep experience designing and operating databases — both OLTP (Postgres/MySQL) and at least one OLAP/analytical engine.
- Solid experience building data lakes, warehouses or lakehouses, and the ETL/ELT pipelines that feed them.
- Advanced Python, plus comfort with at least one of TypeScript/Node, Go or Java for backend services.
- Experience with workflow orchestration (Airflow, Dagster, Prefect or similar) and modern data tooling (dbt, Spark, Kafka, object storage).
- Working experience with LLMs, RAG pipelines, embeddings and vector databases enough to build serious systems around them, not just call an API.
- Strong grasp of cloud infrastructure (AWS/GCP/Azure), containers, CI/CD and basic SRE practice.
- Proficiency in spoken and written Russian.
- Strong problem‑solving mindset and ability to operate in early‑stage, fast‑moving environments with shifting requirements.
- Experience building data and AI products in startups, consulting or research environments.
- Exposure to sectors such as media tech, health tech, agri tech, fintech or other data‑heavy industries.
- Experience with optimisation, forecasting, geospatial data, time‑series at scale, or graph data.
- ML framework experience (PyTorch, TensorFlow, scikit‑learn) and/or model fine‑tuning experience.
- Comfort working across multiple parallel projects and stakeholders.
- Own the data and backend foundations behind a portfolio of technology companies.
- Work directly with founders, operators and investors on real, varied engineering problems.
- Ship AI systems that are actually used in products, not just prototyped.
- Be part of a venture environment where technology, strategy and entrepreneurship intersect.
Backend & Data Engineer in London employer: Raw Ventures Global Limited
At Raw Ventures, we pride ourselves on being an exceptional employer that fosters a dynamic and collaborative work culture. As a Backend & Data Engineer, you will have the unique opportunity to work directly with founders and investors, tackling real-world engineering challenges while contributing to innovative AI systems. Our commitment to employee growth is evident through hands-on projects and exposure to cutting-edge technologies, all within a vibrant venture environment that encourages creativity and strategic thinking.
Contact Detail:
Raw Ventures Global Limited Recruiting Team
StudySmarter Expert Advice🤫
We think this is how you could land Backend & Data Engineer in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to data lakes, ETL pipelines, or backend services. This gives you a chance to demonstrate your hands-on experience and problem-solving abilities.
✨Tip Number 3
Prepare for technical interviews by brushing up on your coding skills and system design knowledge. Practice common interview questions related to databases, APIs, and data architecture to ensure you're ready to impress.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to get noticed by our team. Tailor your application to highlight your relevant experience in backend engineering and data systems, and let us see how you can contribute to our exciting projects.
We think you need these skills to ace Backend & Data Engineer 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 backend engineering experience, data lake projects, and any relevant AI work to show us you’re the perfect fit!
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're excited about this role at StudySmarter. Share specific examples of your past work with databases, ETL pipelines, or backend services to give us a taste of what you can bring to the team.
Show Off Your Projects:If you've worked on any cool projects related to data engineering or AI, don’t hesitate to mention them! We love seeing real-world applications of your skills, so include links or descriptions of your work.
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 the StudySmarter family!
How to prepare for a job interview at Raw Ventures Global Limited
✨Know Your Tech Stack
Make sure you’re well-versed in the technologies mentioned in the job description, like Python, TypeScript, and the various databases. Brush up on your experience with ETL/ELT pipelines and data lakes, as these will likely come up during the interview.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific challenges you've faced in previous roles, especially those related to backend engineering and data architecture. Use the STAR method (Situation, Task, Action, Result) to structure your answers and highlight your problem-solving mindset.
✨Understand the Business Context
Familiarise yourself with the company’s portfolio and how they leverage data and AI. Being able to discuss how your role as a Backend & Data Engineer can impact their business will show that you’re not just technically skilled but also aligned with their goals.
✨Ask Insightful Questions
Prepare thoughtful questions about the team dynamics, the types of projects you'll be working on, and how success is measured in this role. This shows your genuine interest in the position and helps you gauge if it’s the right fit for you.
