At a Glance
- Tasks: Lead data profiling and cleansing to ensure accurate data across the business.
- Company: Renowned rug company in Glossop with a strong reputation.
- Benefits: Hybrid working model, pension scheme, and colleague discounts.
- Why this job: Make a real impact on data integrity in a supportive environment.
- Qualifications: Experience in data governance and knowledge of analytics tools.
- Other info: 12-month fixed term role with opportunities for growth.
The predicted salary is between 36000 - 60000 £ per year.
A renowned rug company in Glossop is seeking a Data Analyst for a 12-month fixed term role. You will lead data profiling and cleansing efforts, ensuring accurate and usable data across the business.
Ideal candidates will have experience in data governance and a strong understanding of data analytics tools.
This role offers a hybrid working model with supportive benefits such as a pension scheme and colleague discounts.
Data Integrity Analyst (12-Month FTC) in Glossop employer: Flair Rugs
Contact Detail:
Flair Rugs Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Integrity Analyst (12-Month FTC) in Glossop
✨Tip Number 1
Network like a pro! Reach out to current or former employees of the rug company on LinkedIn. A friendly chat can give us insider info and maybe even a referral!
✨Tip Number 2
Prepare for the interview by brushing up on data governance and analytics tools. We should be ready to showcase our skills with real-life examples that highlight our experience in data profiling and cleansing.
✨Tip Number 3
Don’t forget to tailor your approach! When we apply through our website, make sure to highlight how our skills align with the job description. Show them we’re the perfect fit for their team!
✨Tip Number 4
Follow up after the interview! A quick thank-you email can keep us fresh in their minds and show our enthusiasm for the role. Let’s make sure they remember us!
We think you need these skills to ace Data Integrity Analyst (12-Month FTC) in Glossop
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in data governance and analytics tools. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects or achievements!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about data integrity and how you can contribute to our team. Keep it engaging and personal – we love to see your personality come through.
Showcase Your Data Skills: In your application, mention specific data profiling and cleansing techniques you’ve used in the past. We’re looking for candidates who can demonstrate their expertise, so don’t hold back on the details!
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 – just follow the prompts!
How to prepare for a job interview at Flair Rugs
✨Know Your Data Tools
Familiarise yourself with the data analytics tools mentioned in the job description. Be ready to discuss your experience with them and how you've used them in past roles. This shows you’re not just a fit on paper but also have practical knowledge.
✨Showcase Your Data Governance Experience
Prepare examples of how you've contributed to data governance in previous positions. Highlight specific projects where you ensured data accuracy and integrity, as this will resonate well with the company's needs.
✨Understand the Company’s Data Needs
Research the rug company and its operations. Understanding their business model and how data impacts their success will help you tailor your answers and demonstrate your genuine interest in the role.
✨Prepare Questions About the Role
Think of insightful questions to ask about the data profiling and cleansing efforts they currently have in place. This not only shows your enthusiasm for the position but also helps you gauge if the role aligns with your career goals.