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- Tasks: Build scalable data platforms for credit risk and analytics using Spark, Scala, and Python.
- Company: Global technology leader in the Risk Technology sector.
- Benefits: Flexible hybrid working, competitive salary, and opportunities for innovation.
- Why this job: Join a dynamic team and make a real impact in the tech industry.
- Qualifications: 16+ years of experience in risk and financial data with strong technical skills.
- Other info: Exciting opportunity to innovate and grow in a fast-paced environment.
The predicted salary is between 57600 - 84000 £ per year.
A global technology company is seeking a Senior Big Data Engineer to join their Risk Technology group in Greater London. The role focuses on building scalable data platforms for credit risk and analytics, requiring deep expertise in Spark, Scala, and Python. Candidates must have over 16 years of experience and extensive knowledge in risk and financial data. This hybrid position promotes flexibility with a blend of office, client site, and home working. Join a dynamic team and help innovate in the technology sector.
Senior Big Data Engineer - Spark/Scala/Python (Risk Tech) employer: Capgemini
Contact Detail:
Capgemini Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Big Data Engineer - Spark/Scala/Python (Risk Tech)
✨Tip Number 1
Network like a pro! Reach out to your connections in the tech industry, especially those in risk tech. A friendly chat can lead to insider info about job openings or even referrals.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects in Spark, Scala, and Python. This is your chance to demonstrate your expertise and make a lasting impression on potential employers.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice common interview questions related to big data engineering and be ready to discuss your experience with credit risk and analytics.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities waiting for you, and applying directly can give you an edge. Plus, it’s super easy to keep track of your applications!
We think you need these skills to ace Senior Big Data Engineer - Spark/Scala/Python (Risk Tech)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Spark, Scala, and Python. We want to see how your skills align with the role, so don’t be shy about showcasing your expertise in building scalable data platforms.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about risk technology and how your background makes you the perfect fit for our team. We love hearing your story!
Showcase Relevant Projects: If you've worked on projects related to credit risk or analytics, make sure to mention them. We’re interested in seeing how you’ve applied your skills in real-world scenarios, so don’t hold back!
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 this exciting opportunity in our dynamic team!
How to prepare for a job interview at Capgemini
✨Know Your Tech Inside Out
Make sure you brush up on your Spark, Scala, and Python skills. Be ready to discuss specific projects where you've used these technologies, as well as any challenges you faced and how you overcame them.
✨Understand Risk and Financial Data
Since the role focuses on credit risk and analytics, it’s crucial to have a solid grasp of risk management principles and financial data. Prepare to talk about how you've applied this knowledge in previous roles.
✨Showcase Your Problem-Solving Skills
Be prepared to tackle technical problems during the interview. Practice coding challenges or system design questions that relate to big data engineering, and explain your thought process clearly.
✨Emphasise Flexibility and Teamwork
This hybrid role requires collaboration across different settings. Share examples of how you've successfully worked in diverse teams and adapted to various work environments, whether in the office or remotely.
