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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 80000 - 100000 £ 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 City of London employer: Develop
Contact Detail:
Develop Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineer in City of London
✨Tip Number 1
Network like a pro! Reach out to folks in the data engineering space on LinkedIn or at meetups. We can’t stress enough how personal connections can lead to job opportunities that aren’t even advertised.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data pipelines and projects. We love seeing real-world applications of your work, so make sure to highlight any experience with Python, SQL, and cloud platforms.
✨Tip Number 3
Prepare for those interviews! Brush up on your technical skills and be ready to discuss your approach to building reliable data pipelines. We recommend practising common data engineering scenarios and problem-solving questions.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. We’re always on the lookout for talented data engineers who can help us build amazing AI systems.
We think you need these skills to ace Data Engineer in City of 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, make sure to include them. We love seeing real-world applications of your skills, especially those involving data structuring and AI systems.
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!
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. Being able to explain your thought process behind designing data models will impress the interviewers.
✨Showcase Your Collaboration Skills
This role involves working closely with various specialists, so highlight your experience in collaborative environments. Prepare examples of how you’ve successfully worked with engineers and stakeholders to deliver projects. Emphasise your ability to communicate complex ideas clearly and effectively.
✨Demonstrate Your Problem-Solving Mindset
Expect questions that assess your ability to handle ambiguity and solve problems. Think of scenarios where you faced challenges in data engineering and how you overcame them. Show that you can think critically and adapt to changing requirements, which is crucial in a consulting-style delivery model.
✨Prepare for Technical Questions
Be ready for technical assessments that may involve coding or system design. Practice common data engineering problems and be prepared to explain your solutions. Familiarise yourself with cloud platforms like AWS, Azure, or GCP, as they might ask about your experience with these technologies.
