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- Tasks: Prepare datasets and build scalable pipelines for predictive modelling.
- Company: Leading technology consulting firm in Greater London with a focus on innovation.
- Benefits: Hybrid working model, competitive salary, and opportunities for professional growth.
- Why this job: Join a dynamic team and make an impact on data-driven initiatives.
- Qualifications: Strong expertise in Python and Scala, plus knowledge of big data frameworks.
- Other info: Collaborative environment with a mix of office, client, and home work.
The predicted salary is between 36000 - 60000 £ per year.
A leading technology consulting firm in Greater London is seeking a Data Engineer to support predictive modeling and data-driven initiatives. You will prepare datasets, build scalable pipelines, and collaborate with product and engineering teams.
Ideal candidates will have:
- Strong expertise in Python and Scala
- Knowledge of big data frameworks
- Cloud platform familiarity
This role embraces a hybrid working model, blending office, client, and home work environments.
Data Engineer: Python & Scala for ML Pipelines (Hybrid) employer: Capgemini
Contact Detail:
Capgemini Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineer: Python & Scala for ML Pipelines (Hybrid)
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your Python and Scala projects, especially those related to ML pipelines. This will give potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by brushing up on big data frameworks and cloud platforms. We recommend doing mock interviews with friends or using online resources to get comfortable with common questions.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Data Engineer: Python & Scala for ML Pipelines (Hybrid)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Python and Scala, especially in relation to building ML pipelines. We want to see how your skills align 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 Data Engineer position and how your background fits the job description. We love seeing genuine enthusiasm for the role.
Showcase Your Big Data Knowledge: If you've worked with big data frameworks or cloud platforms, make sure to mention them! We’re looking for candidates who can hit the ground running, so any relevant experience will definitely catch our eye.
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 to do!
How to prepare for a job interview at Capgemini
✨Know Your Tech Stack
Make sure you brush up on your Python and Scala skills before the interview. Be ready to discuss how you've used these languages in past projects, especially in building scalable data pipelines or working with predictive modelling.
✨Familiarise with Big Data Frameworks
Since the role requires knowledge of big data frameworks, take some time to review the ones you've worked with. Be prepared to explain how you've implemented them in real-world scenarios, as this will show your practical experience.
✨Understand Cloud Platforms
Get a good grasp of the cloud platforms relevant to the job. Whether it's AWS, Azure, or Google Cloud, be ready to discuss how you've leveraged these platforms for data engineering tasks, as this is crucial for the hybrid working model.
✨Collaboration is Key
This role involves working closely with product and engineering teams, so think of examples where you've successfully collaborated in the past. Highlight your communication skills and how you can bridge the gap between technical and non-technical team members.
