At a Glance
- Tasks: Own and architect data infrastructure for a fast-growing AI company.
- Company: Dynamic AI startup in London with strong backing and paying customers.
- Benefits: Competitive salary, ownership from day one, and opportunity to shape the future.
- Other info: Ground floor opportunity with potential for significant career growth.
- Why this job: Make a real impact by building scalable data systems in a thriving environment.
- Qualifications: 7+ years in data engineering, strong Python and SQL skills required.
The predicted salary is between 80000 - 100000 £ per year.
I'm working with a fast-growing AI company in London who are looking for their first senior data hire a Lead Data Engineer to own and architect their data infrastructure from the ground up. You'd be coming in with real ownership from day one, working directly with the founders and core engineering team. The company is already live with paying customers, backed by experienced investors, and scaling fast.
What you'll be building:
- Scalable data pipelines and ingestion architecture for both operational and AI workloads
- Batch and real-time data processing systems at scale
- Vector search and retrieval systems powering AI features
- ML data pipelines supporting model training and inference
- Monitoring, observability and data quality frameworks
- The foundations of a future data engineering team
What they're looking for:
- 7+ years in data engineering or backend engineering
- Strong Python and SQL fundamentals
- PostgreSQL and NoSQL database experience
- Hands-on experience with vector databases (Qdrant, Milvus, pgvector or similar)
- Familiarity with Spark, Airflow, Kafka or Elasticsearch is a plus
- Cloud experience across GCP, AWS or Azure
- Someone who has built greenfield data systems and thrives in fast-moving environments
- Startup experience or a genuine appetite for it
Why this role stands out: This is a ground floor opportunity at an AI company with real product-market fit, strong backing, and a clear path to scale. You'd be shaping architecture decisions that will define how the platform operates for years to come.
Lead Data Engineer employer: Arrows
Join a dynamic AI company in London that offers an exceptional work environment where innovation thrives. As a Lead Data Engineer, you'll enjoy the unique opportunity to shape the data infrastructure from the ground up, working closely with founders and a passionate engineering team. With a strong focus on employee growth, competitive benefits, and a culture that values ownership and creativity, this role promises a rewarding career path in a fast-paced, supportive setting.
StudySmarter Expert Advice🤫
We think this is how you could land Lead Data Engineer
✨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!
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.
