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- Tasks: Design and build cloud-based data pipelines for advanced analytics and machine learning.
- Company: Dynamic financial services firm focused on innovation and collaboration.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Why this job: Join a small, agile team and make a real impact in data engineering.
- Qualifications: Strong Python skills, Azure experience, and knowledge of Snowflake data warehousing.
The predicted salary is between 36000 - 60000 £ per year.
A financial services firm is seeking a Data Engineer to expand their capabilities in building modern, scalable data platforms. The role involves designing cloud-based data pipelines and utilizing tools such as dbt and Airflow.
The ideal candidate will possess strong Python skills, experience with Azure, and an understanding of data warehousing with Snowflake.
This position provides opportunities for impact within a small, agile team dedicated to optimizing data for advanced analytics and machine learning applications, promoting a collaborative and innovative culture.
Data Engineer - Azure Cloud Data Pipelines & ML employer: Cerberus Capital Management
Contact Detail:
Cerberus Capital Management Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineer - Azure Cloud Data Pipelines & ML
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those already working with Azure and data pipelines. A friendly chat can lead to insider info about job openings or even a referral.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects with dbt, Airflow, and Python. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for the interview by brushing up on your technical knowledge. Be ready to discuss your experience with Azure and Snowflake, and think of examples where you've optimised data for analytics or machine learning.
✨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 - Azure Cloud Data Pipelines & ML
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Azure, Python, and data warehousing. 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! Tell us why you’re excited about the Data Engineer role and how you can contribute to our team. Keep it engaging and personal – we love a good story!
Showcase Your Technical Skills: Since this role involves building data pipelines, make sure to mention your experience with tools like dbt and Airflow. We’re keen to see how you’ve used these in past projects, so give us the details!
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 Cerberus Capital Management
✨Know Your Tech Stack
Make sure you’re well-versed in Azure, dbt, and Airflow. Brush up on your Python skills too! Be ready to discuss how you've used these tools in past projects, as this will show your practical experience and understanding of the tech stack.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific challenges you've faced in data engineering and how you overcame them. Use examples that highlight your ability to design scalable data pipelines and optimise data for analytics and machine learning.
✨Understand the Company’s Needs
Research the financial services firm and understand their data needs. Think about how your skills can help them build modern data platforms. This shows that you’re not just looking for any job, but that you’re genuinely interested in contributing to their success.
✨Emphasise Collaboration
Since the role involves working within a small, agile team, be prepared to discuss your experience in collaborative environments. Share examples of how you’ve worked with others to achieve common goals, especially in optimising data for advanced analytics.
