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- Tasks: Analyse data to drive business insights and oversee retail incentive schemes.
- Company: Leading global beauty company with a supportive culture.
- Benefits: Competitive salary, development opportunities, and a vibrant work environment.
- Why this job: Join a dynamic team and make an impact in the beauty industry.
- Qualifications: Experience in data management and understanding of financial processes.
- Other info: Great career growth potential in a collaborative setting.
The predicted salary is between 28800 - 48000 £ per year.
A leading global beauty company is seeking a Data Analyst in London. This role combines business analysis with data system capabilities, overseeing retail incentive schemes and providing insights for strategic objectives. The successful candidate will engage with multiple teams to implement robust data solutions.
Required qualifications include proven data management experience and familiarity with financial processes. Enjoy a supportive culture rich in development opportunities, along with a competitive compensation package.
Commercial Data Analyst: Power BI & Incentive Analytics employer: PUIG
Contact Detail:
PUIG Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Commercial Data Analyst: Power BI & Incentive Analytics
✨Tip Number 1
Network like a pro! Reach out to people in the beauty industry or data analytics field on LinkedIn. A friendly chat can open doors and give you insights that might just land you that interview.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your Power BI projects or any data analysis work you've done. This visual proof of your capabilities can really impress potential employers.
✨Tip Number 3
Prepare for those interviews! Research the company and think about how your experience aligns with their goals, especially around retail incentive schemes. Tailoring your answers can make a huge difference.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities waiting for you, and applying directly can sometimes give you an edge over other candidates.
We think you need these skills to ace Commercial Data Analyst: Power BI & Incentive Analytics
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your data management experience and familiarity with financial processes. We want to see how your skills align with the role of a Commercial Data Analyst, so don’t be shy about showcasing relevant projects!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re excited about the role and how you can contribute to our team. We love seeing genuine enthusiasm for the beauty industry and data analytics.
Showcase Your Power BI Skills: Since this role involves Power BI, make sure to mention any specific projects or experiences where you've used this tool. We’re keen to see how you’ve leveraged data visualisation to drive insights in previous roles.
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’re considered for the role. Plus, it’s super easy to do!
How to prepare for a job interview at PUIG
✨Know Your Data Tools
Make sure you're well-versed in Power BI and any other data management tools mentioned in the job description. Brush up on your skills and be ready to discuss how you've used these tools in past roles to drive insights and support business decisions.
✨Understand Retail Incentive Schemes
Familiarise yourself with retail incentive schemes and how they impact business performance. Be prepared to share examples of how you've analysed similar schemes in the past and the insights you derived from them.
✨Showcase Your Analytical Skills
During the interview, highlight your experience with data analysis and financial processes. Use specific examples to demonstrate how your analytical skills have led to actionable insights that align with strategic objectives.
✨Engage with Team Dynamics
Since this role involves working with multiple teams, be ready to discuss your experience collaborating across departments. Share examples of how you've successfully communicated complex data findings to non-technical stakeholders to drive decision-making.
