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- Tasks: Design and optimise data solutions using Azure technologies like Databricks and Data Lake.
- Company: Join a forward-thinking client in the tech industry with a focus on innovation.
- Benefits: Hybrid working model, competitive pay, and opportunities for skill enhancement.
- Other info: Initial 3-month contract with potential for growth and development.
- Why this job: Make an impact by leveraging your data skills in a dynamic environment.
- Qualifications: Experience with SQL, Python, Spark, and CI/CD practices is essential.
The predicted salary is between 50000 - 65000 £ per year.
Our client is looking for an Azure data engineer. You will have extensive experience of Azure Databricks, Azure Data Lake Storage, and Azure Data Factory.
Advanced proficiency in SQL, Python, and Spark (PySpark), with a strong focus on performance optimization and distributed processing.
Proven experience in CI/CD practices using industry-standard tools (e.g., GitHub Actions, Azure DevOps).
Strong understanding of data architecture principles and cloud-native design patterns.
This is a 3 month contract initially. It is outside IR35 on a hybrid working model, 2 to 3 days in London per week. Candidates will need to be based in the UK.
For the application please send across an updated CV.
Azure Data Engineer employer: LA International
Contact Detail:
LA International Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Azure Data Engineer
✨Tip Number 1
Network like a pro! Reach out to your connections in the data engineering field, especially those who work with Azure. 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 with Azure Databricks, Data Lake Storage, and Data Factory. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your SQL, Python, and Spark knowledge. Be ready to discuss performance optimisation and distributed processing, as these are key areas for an Azure Data Engineer.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities that might just be the perfect fit for you. Plus, it’s a great way to stay updated on new roles tailored to your skills.
We think you need these skills to ace Azure Data Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Azure Databricks, Data Lake Storage, and Data Factory. We want to see how your skills in SQL, Python, and Spark shine through, so don’t hold back!
Showcase Your CI/CD Knowledge: Mention any experience you have with CI/CD practices and tools like GitHub Actions or Azure DevOps. We love seeing candidates who are up-to-date with industry standards, so let us know what you've done!
Focus on Performance Optimisation: Since performance optimisation is key for this role, include specific examples of how you've improved data processing in your previous projects. We’re all about results, so show us what you can do!
Apply Through Our Website: Don’t forget to submit your application through our website! It’s the best way for us to keep track of your application and ensure it gets the attention it deserves.
How to prepare for a job interview at LA International
✨Know Your Azure Inside Out
Make sure you brush up on your knowledge of Azure Databricks, Azure Data Lake Storage, and Azure Data Factory. Be ready to discuss how you've used these tools in past projects, focusing on specific challenges you faced and how you overcame them.
✨Show Off Your Coding Skills
Since advanced proficiency in SQL, Python, and Spark (PySpark) is crucial, prepare to demonstrate your coding skills. You might be asked to solve a problem on the spot, so practice common data manipulation tasks and performance optimisation techniques beforehand.
✨CI/CD Practices Are Key
Familiarise yourself with CI/CD practices and be prepared to discuss your experience with tools like GitHub Actions and Azure DevOps. Share examples of how you've implemented these practices in previous roles to streamline workflows and improve deployment efficiency.
✨Understand Data Architecture Principles
Have a solid grasp of data architecture principles and cloud-native design patterns. Be ready to explain how these concepts apply to the role and how you've utilised them in your work to create scalable and efficient data solutions.