At a Glance
- Tasks: Architect and build scalable data pipelines for cutting-edge AI products.
- Company: Early-stage, well-funded AI business transforming a traditional industry.
- Benefits: Competitive salary, equity options, and the chance to shape the future of AI.
- Other info: Fast-paced startup environment with opportunities for mentorship and growth.
- Why this job: Join a small team and own the data architecture from day one.
- Qualifications: 7+ years in data engineering, strong Python skills, and experience with modern databases.
The predicted salary is between 80000 - 100000 £ per year.
I'm currently partnered with an early-stage, well-funded AI business that is building the operating system for a large, traditionally under-digitised industry, and looking to hire their first Lead Data Engineer into a small, high-impact team. This is a broad and genuinely technical role sitting at the heart of the data function, responsible for architecting and scaling the systems powering their AI products. Already live with pilot customers managing 14,000+ units, this is a rare opportunity to join early and own the data architecture from day one, working closely with AI engineers, backend engineers and product teams to translate business needs into real-time, AI-ready data infrastructure. The environment suits someone who enjoys building from scratch, takes pride in architectural rigour, and wants to be the defining technical voice behind a company's data foundations as it scales toward a category-defining AI platform.
What you'll be doing:
- Architecting and building scalable data pipelines and infrastructure to support AI and product systems
- Designing data ingestion, transformation and storage architectures for operational and AI workloads
- Developing and managing batch and real-time data pipelines
- Building and optimising systems for vector search, retrieval and ML data pipelines
- Ensuring data reliability, security and governance across the platform
- Implementing monitoring, observability and data quality frameworks
- Contributing to technical architecture decisions and long-term data strategy
- Helping build and mentor the future data engineering team as the company scales
Tech stack includes: Python, PostgreSQL, MongoDB, vector databases (Qdrant, Milvus or pgvector), Apache Spark, Apache Airflow, Kafka, Elasticsearch/OpenSearch, Pandas, Polars.
What they're looking for:
- 7+ years of experience in data engineering or backend engineering
- Strong experience designing and building data pipelines and distributed data systems
- Experience with relational databases (PostgreSQL preferred) and NoSQL databases
- Experience with vector databases used in modern AI systems
- Strong programming experience in Python
- Comfortable working in a fast-moving, high-ownership startup environment
Lead Data Engineer in London employer: Arrows
Arrows is an exceptional employer that fosters a dynamic and collaborative work culture, perfect for those passionate about media technology. With a strong emphasis on employee growth, we offer numerous opportunities for professional development and advancement, all while working in a vibrant location that encourages innovation and creativity. Join us to lead impactful projects and make a meaningful contribution in the broadcasting sector.
StudySmarter Expert Advice🤫
We think this is how you could land Lead Data Engineer in London
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Arrows!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Lead Data Engineer at Arrows.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Arrows.
✨Apply Directly through Our Website
When you find a suitable opening like Lead Data Engineer at Arrows, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Lead Data 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.
