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- Tasks: Join a small team to enhance data quality through cleansing and validation.
- Company: Be part of a dynamic networking company in the finance sector.
- Benefits: Enjoy a competitive salary and flexible work with just one day in London each month.
- Why this job: Make a real impact on data processes while working in a collaborative environment.
- Qualifications: Strong SQL skills are essential; Python knowledge is a bonus.
- Other info: This is a unique opportunity to shape data quality from the ground up.
The predicted salary is between 32000 - 42000 £ per year.
We are working on a Data Quality Analyst role with a networking company in the finance industry based in London (1 day a month). They are a small team of 3 people and don't currently have a hands-on Data Quality Analyst, so need someone with strong experience in SQL and ideally Python.
You will work within the data warehouse to cleanse data and work across the end-to-end data process and improve the overall data quality of the business.
Salary: £40K-£50K
Location: London (1 day a month)
Skills needed: SQL
Nice to have: Python
Desired Skills and Experience: SQL, Python, Data Cleansing, Data Validation, Data Quality
Data Quality Analyst employer: LinkedIn
Contact Detail:
LinkedIn Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Quality Analyst
✨Tip Number 1
Familiarise yourself with the specific data quality challenges faced in the finance industry. Understanding the nuances of financial data can give you an edge during discussions and interviews.
✨Tip Number 2
Brush up on your SQL skills by working on real-world data cleansing projects. Being able to demonstrate your practical experience with SQL will be crucial, especially since they are looking for someone to hit the ground running.
✨Tip Number 3
If you have experience with Python, consider building a small project that showcases your ability to automate data quality checks. This can be a great talking point in interviews and shows your initiative.
✨Tip Number 4
Network with professionals in the finance and data quality fields. Engaging with others in the industry can provide insights into the role and may even lead to referrals, increasing your chances of landing the job.
We think you need these skills to ace Data Quality Analyst
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with SQL and any relevant Python skills. Include specific examples of data cleansing and validation projects you've worked on to demonstrate your expertise.
Craft a Compelling Cover Letter: In your cover letter, explain why you're interested in the Data Quality Analyst role and how your skills align with the company's needs. Mention your experience in improving data quality and your ability to work within a small team.
Showcase Relevant Projects: If you have worked on specific projects related to data quality, cleansing, or validation, be sure to mention these in your application. Highlight the impact of your work and any tools or methodologies you used.
Proofread Your Application: Before submitting your application, carefully proofread all documents for spelling and grammatical errors. A polished application reflects your attention to detail, which is crucial for a Data Quality Analyst.
How to prepare for a job interview at LinkedIn
✨Showcase Your SQL Skills
Since the role requires strong experience in SQL, be prepared to discuss your previous projects and how you've used SQL to improve data quality. Consider bringing examples of complex queries you've written or challenges you've overcome using SQL.
✨Demonstrate Data Cleansing Techniques
Be ready to explain your approach to data cleansing and validation. Discuss specific methods or tools you’ve used in the past to ensure data accuracy and integrity, as this will show your practical knowledge in the field.
✨Highlight Your Teamwork Experience
As you'll be joining a small team, emphasise your ability to work collaboratively. Share examples of how you've successfully worked with others to achieve common goals, especially in data-related projects.
✨Prepare for Technical Questions
Expect technical questions related to both SQL and Python. Brush up on key concepts and be ready to solve problems on the spot. Practising common interview questions can help you feel more confident during the technical portion of the interview.
