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For Companies At a Glance
- Tasks: Analyze insurance data and create insightful reports using PowerBI and SQL.
- Company: Join a global insurer making waves in the finance sector.
- Benefits: Enjoy hybrid working and competitive pay up to £650pd outside IR35.
- Why this job: Dive into impactful projects while enhancing your data skills in a dynamic environment.
- Qualifications: Strong background in insurance data analysis and technical skills in DBT, Snowflake, and PowerBI required.
- Other info: Initial 6-month contract with potential for extension.
The predicted salary is between 54000 - 78000 £ per year.
Data Analyst / Insurance / PowerBI / Snowflake / Finance Initial 6mth contract, hybrid working, up to £650pd outside IR35 KEY SKILLS: Data Modelling & Data Structures experience Technical exposure of PowerBI, SQL, Snowflake Data analysis experience within the insurance sector THE CANDIDATE: The successful candidate will have a strong insurance data analysis background. You will have technical experience using DBT Modelling, Snowflake, PowerBI & SQL. You will demonstrate experience building data warehouses / repositories with exposure to finance related projects. THE CLENT: Global insurer …
Data Analyst employer: Red 10
Contact Detail:
Red 10 Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Analyst
✨Tip Number 1
Make sure to highlight your experience in the insurance sector during networking events or conversations. This will help you connect with professionals who can refer you to opportunities at StudySmarter.
✨Tip Number 2
Familiarize yourself with the latest trends and tools in data analysis, especially PowerBI and Snowflake. Being able to discuss recent developments or projects you've worked on can set you apart in interviews.
✨Tip Number 3
Join online forums or groups focused on data analysis in the insurance industry. Engaging with others in the field can provide valuable insights and potentially lead to job referrals.
✨Tip Number 4
Consider creating a portfolio showcasing your data modeling and analysis projects. This can be a great conversation starter during interviews and demonstrate your hands-on experience.
We think you need these skills to ace Data Analyst
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with data modelling, PowerBI, SQL, and Snowflake. Emphasize any relevant projects in the insurance sector to align with the job requirements.
Craft a Strong Cover Letter: In your cover letter, explain why you are a great fit for the role. Mention specific experiences that demonstrate your technical skills and your background in finance-related projects.
Showcase Relevant Projects: If you have worked on projects involving DBT Modelling or building data warehouses, include these in your application. Provide details on your role and the impact of your work.
Proofread Your Application: Before submitting, carefully proofread your application for any errors. A well-written application reflects your attention to detail, which is crucial for a Data Analyst role.
How to prepare for a job interview at Red 10
✨Showcase Your Technical Skills
Make sure to highlight your experience with PowerBI, SQL, and Snowflake during the interview. Be prepared to discuss specific projects where you utilized these tools, especially in the context of data modeling and building data warehouses.
✨Demonstrate Industry Knowledge
Since the role is within the insurance sector, it's crucial to demonstrate your understanding of industry-specific data challenges. Prepare examples of how you've tackled data analysis problems in insurance or finance-related projects.
✨Prepare for Scenario-Based Questions
Expect scenario-based questions that assess your problem-solving skills. Think about past experiences where you had to analyze complex data sets or improve data structures, and be ready to explain your thought process.
✨Ask Insightful Questions
At the end of the interview, ask questions that show your interest in the company and the role. Inquire about their current data projects, the tools they use, or how they measure success in data analysis within the insurance sector.
