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- Tasks: Design and build scalable data pipelines for AI safety monitoring.
- Company: Leading AI safety organisation in Greater London.
- Benefits: Competitive compensation and a hybrid work environment.
- Why this job: Make a real impact on user well-being through data engineering.
- Qualifications: 3+ years experience, SQL and Python proficiency required.
- Other info: Collaborate with engineers and data scientists in a dynamic team.
The predicted salary is between 42000 - 60000 £ per year.
A leading AI safety organization in Greater London seeks a Data Engineer to design and build robust data infrastructures for safety monitoring and user well-being. In this role, you will collaborate with engineers, data scientists, and policy teams to ensure reliable analytics that inform model behavior and safety interventions.
Ideal candidates have:
- 3+ years of experience
- Proficiency in SQL and Python
- Familiarity with modern data stack tools
This position offers competitive compensation and a hybrid work environment.
Data Engineer: Architect scalable data pipelines employer: Alcides Fonseca
Contact Detail:
Alcides Fonseca Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineer: Architect scalable data pipelines
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those already working at the company you're eyeing. A friendly chat can give you insider info and maybe even a referral!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data pipelines or any relevant projects. This is your chance to demonstrate your SQL and Python prowess in a way that a CV just can't.
✨Tip Number 3
Prepare for the interview by brushing up on common data engineering scenarios. Think about how you'd tackle real-world problems related to safety monitoring and user well-being—this will impress the hiring team!
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who take that extra step!
We think you need these skills to ace Data Engineer: Architect scalable data pipelines
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with SQL and Python, as well as any relevant projects you've worked on. We want to see how your skills align with the role of a Data Engineer in building robust data infrastructures.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about AI safety and how your background makes you a great fit for our team. Share specific examples of your work that demonstrate your ability to collaborate with engineers and data scientists.
Showcase Your Problem-Solving Skills: In your application, highlight instances where you've tackled complex data challenges. We love seeing how you approach problems and come up with innovative solutions, especially in the context of user well-being and safety monitoring.
Apply Through Our Website: We encourage you to submit your application through our website for the best chance of being noticed. It helps us keep everything organised and ensures your application gets to the right people quickly!
How to prepare for a job interview at Alcides Fonseca
✨Know Your Data Tools
Make sure you brush up on your SQL and Python skills before the interview. Be ready to discuss how you've used these tools in past projects, especially in building data pipelines or infrastructures.
✨Showcase Collaboration Skills
Since this role involves working with engineers, data scientists, and policy teams, prepare examples of how you've successfully collaborated in the past. Highlight any cross-functional projects where you contributed to safety monitoring or user well-being.
✨Understand the Company’s Mission
Research the AI safety organisation and understand their goals. Be prepared to discuss how your work as a Data Engineer can contribute to their mission of ensuring user safety and well-being through reliable analytics.
✨Prepare for Technical Questions
Expect technical questions related to data architecture and pipeline design. Practice explaining your thought process when designing scalable solutions, and be ready to tackle hypothetical scenarios that test your problem-solving skills.