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- Tasks: Build and scale data pipelines for a cutting-edge trading analytics engine.
- Company: Block MB, a forward-thinking company in Greater London.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on innovation and career advancement.
- Why this job: Join a dynamic team and make an impact in the world of trading analytics.
- Qualifications: Experience with cloud platforms, Python or Rust, and scalable data solutions.
The predicted salary is between 60000 - 80000 £ per year.
Block MB in Greater London seeks a skilled Data Engineer to build a next-generation trading analytics engine. This role focuses on owning and scaling data pipelines within a collaborative Data Science team, addressing complex trading data for actionable insights.
The ideal candidate brings strong experience in cloud-based platforms like Snowflake, advanced Python or Rust programming, and expertise in scalable data pipelines. Familiarity with containerisation and data visualisation tools is preferred.
Senior Data Engineer, Trade Analytics Platform in London employer: Block MB
Contact Detail:
Block MB Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Data Engineer, Trade Analytics Platform in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those working at Block MB or similar companies. A friendly chat can open doors and give you insider info on what they're really looking for.
✨Tip Number 2
Show off your skills! If you've got a portfolio of projects or contributions to open-source, make sure to highlight them. We love seeing real-world applications of your expertise in cloud platforms and data pipelines.
✨Tip Number 3
Prepare for the technical interview! Brush up on your Python or Rust skills and be ready to discuss your experience with scalable data pipelines. We want to see how you tackle complex problems, so practice explaining your thought process.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in joining our team at Block MB.
We think you need these skills to ace Senior Data Engineer, Trade Analytics Platform in London
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your experience with cloud-based platforms like Snowflake and your programming prowess in Python or Rust. We want to see how you can contribute to our next-gen trading analytics engine!
Tailor Your Application: Don’t just send a generic CV! Tailor your application to reflect the specific skills and experiences that match the job description. This shows us you’re genuinely interested in the role and understand what we’re looking for.
Be Clear and Concise: When writing your application, keep it clear and to the point. We appreciate well-structured applications that make it easy for us to see your qualifications and fit for the role.
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 don’t miss out on any important updates from our team!
How to prepare for a job interview at Block MB
✨Know Your Tech Inside Out
Make sure you brush up on your knowledge of cloud-based platforms like Snowflake. Be ready to discuss how you've used these technologies in past projects, and think of specific examples where you’ve built or scaled data pipelines.
✨Show Off Your Coding Skills
Since advanced Python or Rust programming is key for this role, prepare to demonstrate your coding skills. You might be asked to solve a problem on the spot, so practice common data engineering challenges beforehand.
✨Understand the Trading Landscape
Familiarise yourself with trading analytics and the types of data involved. Being able to speak knowledgeably about how data can drive actionable insights in trading will set you apart from other candidates.
✨Be Ready for Collaboration
This role involves working closely with a Data Science team, so be prepared to discuss your experience in collaborative environments. Share examples of how you’ve worked with others to tackle complex data challenges and what tools you used for visualisation.