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- Tasks: Develop and implement data models for banking data using SQL and Hadoop.
- Company: Leading consulting firm with a focus on innovation and collaboration.
- Benefits: Flexible compensation, hybrid work environment, and opportunities for professional growth.
- Why this job: Join a dynamic team and make an impact in the financial services sector.
- Qualifications: Experience in financial services data projects and proficiency in Power BI.
- Other info: Engage with IT and stakeholders in a collaborative setting.
The predicted salary is between 36000 - 60000 £ per year.
A leading consulting firm is seeking a skilled Data Analyst in Milton Keynes to develop and implement a robust data model for banking data. The candidate will work collaboratively in a hybrid environment, utilizing strong SQL and Hadoop skills while engaging with IT and stakeholders.
Key responsibilities include:
- Data mining
- Modelling
- Productionizing models within cloud architectures
The ideal candidate has experience in financial services data projects, alongside proficiency in tools such as Power BI. Flexible compensation based on experience is offered.
Banking Data Analyst — Hadoop/SQL (Hybrid) in Milton Keynes employer: Lorien
Contact Detail:
Lorien Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Banking Data Analyst — Hadoop/SQL (Hybrid) in Milton Keynes
✨Tip Number 1
Network like a pro! Reach out to folks in the banking and data analysis space on LinkedIn. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your SQL and Hadoop projects. This gives potential employers a taste of what you can do, especially in a hybrid environment.
✨Tip Number 3
Prepare for those interviews! Brush up on common data modelling questions and be ready to discuss your experience with financial services data. Confidence is key!
✨Tip Number 4
Don't forget to apply through our website! We love seeing candidates who take the initiative. Plus, it helps us keep track of your application better.
We think you need these skills to ace Banking Data Analyst — Hadoop/SQL (Hybrid) in Milton Keynes
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your SQL and Hadoop skills, as well as any experience in financial services. We want to see how your background aligns with the role, 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 your skills can contribute to our team. We love seeing genuine enthusiasm for data analysis and banking!
Showcase Your Projects: If you've worked on data mining or modelling projects, make sure to mention them! We appreciate candidates who can demonstrate their hands-on experience, especially in cloud architectures and tools like Power BI.
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!
How to prepare for a job interview at Lorien
✨Know Your Data Tools
Make sure you brush up on your SQL and Hadoop skills before the interview. Be ready to discuss specific projects where you've used these tools, as well as any challenges you faced and how you overcame them.
✨Understand the Banking Sector
Familiarise yourself with current trends and challenges in the financial services industry. This will help you demonstrate your knowledge and show that you're genuinely interested in the role and the company.
✨Prepare for Technical Questions
Expect technical questions related to data mining and modelling. Practice explaining your thought process clearly and concisely, as this will showcase your analytical skills and ability to communicate complex ideas effectively.
✨Engage with Stakeholders
Since the role involves collaboration with IT and stakeholders, think of examples where you've successfully worked in a team. Be prepared to discuss how you handle feedback and adapt your models based on stakeholder input.
