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- Tasks: Lead the design and maintenance of data models for a growing personal care brand.
- Company: Exciting personal care brand based in Greater London with a focus on innovation.
- Benefits: Equity, personal development opportunities, and a dynamic work environment.
- Why this job: Join a fast-paced team and make a real impact on data-driven decisions.
- Qualifications: Expertise in data optimization and AWS services required.
- Other info: Collaborate closely with the Director of Data to drive best practices.
The predicted salary is between 36000 - 60000 £ per year.
A growing personal care brand in Greater London is looking for a Data Engineering Architect to lead the design and maintenance of their data models. This role requires expertise in data optimization and AWS services to support scalable solutions for their business operations.
You will work closely with the Director of Data to implement best practices and drive innovation. Ideal candidates will thrive in a fast-paced environment and demonstrate strong technical abilities. The position comes with equity and a focus on personal development.
Analytics Engineer & Data Platform Builder employer: Skin + Me
Contact Detail:
Skin + Me Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Analytics Engineer & Data Platform Builder
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those already working at companies you're eyeing. A friendly chat can open doors and give you insider info on what they're really looking for.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your data models and projects. This is your chance to demonstrate your expertise in data optimisation and AWS services, making you stand out from the crowd.
✨Tip Number 3
Prepare for interviews by practising common questions related to data engineering. We recommend doing mock interviews with friends or using online platforms to get comfortable discussing your technical abilities and past experiences.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Analytics Engineer & Data Platform Builder
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with data models and AWS services. We want to see how your skills align with the role, so don’t be shy about showcasing your technical abilities!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re excited about this role and how you can contribute to our team. Let us know how you thrive in fast-paced environments!
Showcase Your Projects: If you've worked on relevant projects, include them in your application. We love seeing real examples of your work, especially those that demonstrate your expertise in data optimisation and innovative solutions.
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 don’t miss out on any important updates from our team!
How to prepare for a job interview at Skin + Me
✨Know Your Data Inside Out
Make sure you brush up on your data modelling skills and AWS services. Be prepared to discuss specific projects where you've optimised data solutions. This shows you not only understand the theory but can apply it in real-world scenarios.
✨Showcase Your Problem-Solving Skills
Think of examples where you've tackled complex data challenges. The interviewers will want to see how you approach problems, so be ready to walk them through your thought process and the solutions you implemented.
✨Understand Their Business
Research the personal care brand and its operations. Knowing their products and market position will help you tailor your answers and demonstrate how your skills can directly benefit their business.
✨Emphasise Collaboration
Since you'll be working closely with the Director of Data, highlight your teamwork experiences. Share instances where you've successfully collaborated with others to drive innovation and implement best practices in data engineering.
