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For Companies At a Glance
- Tasks: Design and operate innovative data pipelines for cutting-edge AI systems.
- Company: Fast-growing, AI-native consulting business in London.
- Benefits: Competitive salary, hybrid work model, and opportunities for professional growth.
- Why this job: Join a dynamic team and shape the future of AI with meaningful data solutions.
- Qualifications: 5-8 years of data engineering experience, strong Python and SQL skills.
- Other info: Collaborative environment with a focus on real-world impact and client engagement.
The predicted salary is between 68000 - 85000 £ per year.
We’re working with a fast-growing, AI-native consulting business that is building production-grade data foundations to power modern AI systems - including knowledge graphs, semantic layers, retrieval systems, and agentic workflows. This role sits at the heart of that capability. It’s not a traditional reporting or BI data engineering position. The focus is on building high-quality, reliable, meaning-aware data pipelines that make AI systems usable, trustworthy, and scalable in real client environments.
What the role is really about
You’ll be a senior data engineer responsible for designing and operating data pipelines that don’t just move data, but structure it properly - modelling entities, relationships, and constraints so downstream AI systems can reason over it. You’ll work closely with ontology specialists, knowledge graph engineers, and AI engineers to ensure data is semantically aligned, observable, and production ready.
What you’ll be doing
- Designing and owning end-to-end data pipelines using Python, SQL, and modern cloud platforms
- Structuring data around real-world concepts (entities, relationships, taxonomies), not just tables
- Building data foundations that support knowledge graphs, retrieval pipelines (RAG), embeddings, and agent workflows
- Implementing data quality, validation, lineage, and observability so pipelines are reliable and trusted
- Collaborating in a consulting-style delivery model, working directly with engineers and client stakeholders
- Creating reusable patterns, documentation, and standards that others can build on
What we’re looking for
- Strong senior-level data engineering experience (typically 5–8 years)
- Excellent Python and SQL skills, with hands-on pipeline ownership
- Experience working with cloud data platforms (AWS, Azure, or GCP)
- Exposure to semantic modelling, knowledge graphs, or ontology-aligned data (depth can vary, mindset matters most)
- Comfortable working in ambiguous, client-facing environments with high ownership
Data Engineer in London employer: Develop
Contact Detail:
Develop Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineer in London
✨Tip Number 1
Network like a pro! Reach out to your connections in the data engineering field, especially those who work with AI or cloud platforms. A friendly chat can lead to insider info about job openings that aren't even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your best data pipelines and projects. This is your chance to demonstrate how you structure data and solve real-world problems, making it easier for potential employers to see your value.
✨Tip Number 3
Prepare for interviews by brushing up on your Python and SQL skills. Be ready to discuss your experience with cloud platforms and semantic modelling. Practising common interview questions can help you feel more confident and articulate.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got some fantastic opportunities waiting for talented data engineers like you. Plus, applying directly can sometimes give you an edge over other candidates.
We think you need these skills to ace Data Engineer in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the Data Engineer role. Highlight your Python, SQL, and cloud platform experience, and don’t forget to mention any work with knowledge graphs or semantic modelling!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about data engineering and how your background makes you a perfect fit for our team. Be sure to mention your experience in building reliable data pipelines.
Showcase Your Projects: If you've worked on relevant projects, whether in a professional setting or as personal endeavours, include them! We love seeing real-world applications of your skills, especially those that demonstrate your ability to structure data around real-world concepts.
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 you’re keen on joining our awesome team at StudySmarter!
How to prepare for a job interview at Develop
✨Know Your Data Inside Out
Make sure you’re well-versed in data engineering concepts, especially around building data pipelines. Brush up on your Python and SQL skills, and be ready to discuss how you've structured data in previous roles. This will show that you can handle the responsibilities of designing and owning end-to-end data pipelines.
✨Understand Semantic Modelling
Since this role involves working with knowledge graphs and semantic layers, it’s crucial to have a grasp of these concepts. Prepare examples of how you’ve worked with entities, relationships, and constraints in your past projects. This will demonstrate your ability to create meaning-aware data structures.
✨Showcase Your Cloud Experience
Familiarise yourself with the cloud platforms mentioned in the job description, like AWS, Azure, or GCP. Be ready to discuss specific projects where you’ve implemented data solutions on these platforms. This will highlight your hands-on experience and readiness for the role.
✨Prepare for Client-Facing Scenarios
Since the role involves working directly with clients, think about times when you’ve navigated ambiguous situations or collaborated with stakeholders. Prepare anecdotes that showcase your communication skills and ability to take ownership in a consulting-style environment.
