At a Glance
- Tasks: Build and scale data platforms, design ETL pipelines, and develop backend services.
- Company: High-growth investment and technology business with a focus on Data & AI.
- Benefits: Strong long-term growth, high ownership, and exciting AI and data engineering challenges.
- Other info: Collaborative environment with opportunities to mentor and shape best practices.
- Why this job: Join a dynamic team and solve complex engineering problems that impact commercial decisions.
- Qualifications: Strong software engineering skills, experience in data infrastructure, and cloud expertise.
The predicted salary is between 50000 - 70000 £ per year.
I’m currently partnered with a high-growth investment and technology business that is building out its internal Data & AI capability and looking to hire a Data Platform Engineer into a newly evolving team. This is a genuinely broad role sitting at the intersection of software engineering, data infrastructure, automation, and AI. You’d be joining a small, high‑calibre team with real ownership from day one, working closely with senior stakeholders to build systems that directly support commercial decision-making. The environment would suit someone who enjoys solving complex engineering problems, building scalable internal tooling, and working across both backend infrastructure and modern AI workflows.
What you’ll be doing:
- Building and scaling internal data platforms and automation tools
- Designing and maintaining ETL pipelines and production‑grade data workflows
- Developing backend services in Python/FastAPI
- Working across modern frontend tooling including React/Next.js
- Managing cloud infrastructure within GCP
- Contributing to AI‑driven initiatives including LLM workflows and agent‑based systems
- Improving data accessibility, reporting, and operational efficiency across the business
- Mentoring junior team members and helping shape technical best practice
What they’re looking for:
- Strong software engineering fundamentals
- Experience building scalable data infrastructure and pipelines
- Solid cloud experience, ideally within GCP
- Someone comfortable operating across the stack
- Interest in AI systems, automation, and applied ML workflows
- Collaborative mindset with the ability to work closely with technical and non‑technical stakeholders
The role offers strong long‑term growth, a high level of ownership, and the opportunity to work on genuinely interesting AI and data engineering problems within a commercially focused environment.
Data Platform Engineer in London employer: Arrows
Join a dynamic and innovative investment and technology business that prioritises employee growth and collaboration. As a Data Platform Engineer, you'll be part of a high-calibre team where your contributions directly impact commercial decision-making, all within a supportive environment that encourages problem-solving and mentorship. With a focus on cutting-edge AI and data engineering challenges, this role offers not just a job, but a meaningful career path in a thriving sector.
StudySmarter Expert Advice🤫
We think this is how you could land Data Platform Engineer in London
✨Get Involved in Data Science Meetups
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We think you need these skills to ace Data Platform Engineer 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 Arrows, 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 Arrows. 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 Arrows
✨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 Arrows!
✨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.
