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- Tasks: Design and build robust data pipelines for analytics using AWS services.
- Company: Join Graphcore, a leading tech company in Bristol focused on AI innovation.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Be part of a team driving advancements in technology and data management.
- Why this job: Lead impactful AI projects and shape engineering standards in a dynamic environment.
- Qualifications: Strong Python skills, data orchestration experience, and leadership abilities.
The predicted salary is between 60000 - 80000 £ per year.
Graphcore in Bristol is seeking a Lead Data Engineer to enhance its data platform. The role involves designing robust data pipelines for analytics and operational use, focusing on AWS services and implementing best practices in data management.
Ideal candidates will have strong experience in Python and data orchestration, along with leadership skills to influence engineering standards. The position offers an opportunity to contribute to meaningful AI projects and drive improvements across the company.
Lead Data Engineer — Scalable AWS Data Pipelines employer: graphcore
Contact Detail:
graphcore Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead Data Engineer — Scalable AWS Data Pipelines
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those at Graphcore. A friendly chat can sometimes lead to opportunities that aren’t even advertised.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data pipelines and projects. This is your chance to demonstrate your expertise in Python and AWS services in a way that a CV just can't.
✨Tip Number 3
Prepare for the interview by brushing up on leadership scenarios. Think about how you’ve influenced engineering standards in past roles and be ready to share those stories.
✨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 at Graphcore. Plus, we love seeing candidates who take that extra step!
We think you need these skills to ace Lead Data Engineer — Scalable AWS Data Pipelines
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your experience with Python and data orchestration in your application. We want to see how you've used these skills in real-world scenarios, especially in designing data pipelines.
Leadership Matters: Since this role involves influencing engineering standards, don’t forget to mention any leadership experiences you’ve had. We’re looking for candidates who can inspire and guide teams towards best practices.
Tailor Your Application: Take a moment to customise your application for the Lead Data Engineer position. We love seeing how your background aligns with our mission at Graphcore, so make it personal and relevant!
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 to work on meaningful AI projects.
How to prepare for a job interview at graphcore
✨Know Your AWS Inside Out
Make sure you brush up on your knowledge of AWS services, especially those related to data pipelines. Be ready to discuss how you've used these services in past projects and how they can be applied to enhance Graphcore's data platform.
✨Showcase Your Python Skills
Prepare to demonstrate your proficiency in Python during the interview. You might be asked to solve a coding problem or explain how you've used Python for data orchestration in previous roles. Practice common data manipulation tasks to show off your skills.
✨Leadership is Key
Since this role involves influencing engineering standards, think about examples from your past where you've led a team or project. Be ready to discuss your leadership style and how you motivate others to adopt best practices in data management.
✨Connect with Their AI Vision
Graphcore is focused on meaningful AI projects, so do some research on their current initiatives. Be prepared to share your thoughts on how robust data pipelines can drive improvements in AI and how you can contribute to their vision.