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- Tasks: Design and build scalable data pipelines using Azure services in a dynamic cloud environment.
- Company: Join a forward-thinking company in Warwick, UK, focused on data-driven solutions.
- Benefits: Competitive day rate, hands-on experience with cutting-edge technologies, and a collaborative team.
- Why this job: Make an impact by optimising data solutions and driving decision-making across the organisation.
- Qualifications: Strong Azure Data Factory skills and experience in data engineering required.
- Other info: Fast-paced environment with opportunities for professional growth and learning.
The predicted salary is between 50000 - 60000 £ per year.
This role is for an experienced Azure Data Engineer to support the design, development and optimisation of data solutions within a modern cloud-based environment. The successful candidate will contribute to building scalable and reliable data pipelines, enabling data-driven decision-making across the organisation. Working within a fast-paced delivery environment, the role offers exposure to cloud technologies and advanced data platforms, supporting the transformation and integration of large and complex datasets. The position plays a key role in ensuring data quality, accessibility and performance across business-critical systems.
Key responsibilities
- Design, build and maintain scalable data pipelines using Azure data services
- Develop and manage data integration workflows using Azure Data Factory
- Perform data transformation and processing using tools such as Databricks and Python
- Troubleshoot and debug data pipelines to ensure reliability and performance
- Collaborate with stakeholders to understand data requirements and translate them into technical solutions
- Optimise data storage and processing using platforms such as Snowflake
- Ensure data quality, governance and security standards are maintained
- Support deployment and monitoring of data solutions in production environments
- Document data processes and maintain technical documentation
Key skills, knowledge and experience
- Strong experience with Azure data services, including Azure Data Factory
- Advanced expertise in Azure Data Factory development and optimisation
- Experience working with Snowflake for data warehousing solutions
- Hands-on experience with Databricks for large-scale data processing
- Proficiency in Python for data engineering tasks
- Strong SQL knowledge and experience working with relational databases
- Experience in debugging and resolving data pipeline issues
- Understanding of data architecture and best practices in data engineering
- Good communication skills with the ability to work collaboratively
Desirable skills, knowledge and experience
- 6 to 8 years of relevant data engineering experience
- Exposure to additional cloud data platforms or modern data stack tools
- Experience working in agile delivery environments
Azure Data Engineer in Warwick employer: Stott and May
Contact Detail:
Stott and May Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Azure Data Engineer in Warwick
✨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, especially those involving Azure Data Factory and Databricks. This gives potential employers a taste of what you can do beyond your CV.
✨Tip Number 3
Prepare for interviews by brushing up on common data engineering scenarios. Be ready to discuss how you've tackled data pipeline issues or optimised data storage using Snowflake. Real-life examples will make you stand out!
✨Tip Number 4
Don’t forget to apply through our website! We’ve got some fantastic opportunities waiting for talented Azure Data Engineers like you. Plus, it’s a great way to ensure your application gets noticed.
We think you need these skills to ace Azure Data Engineer in Warwick
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Azure data services and any relevant projects you've worked on. We want to see how your skills align with the role, so don’t be shy about showcasing your expertise in data pipelines and cloud technologies!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're the perfect fit for the Azure Data Engineer role. Share specific examples of your work with Azure Data Factory or Databricks, and how you’ve contributed to data-driven decision-making in previous roles.
Showcase Your Problem-Solving Skills: In your application, mention any challenges you've faced while working on data pipelines and how you resolved them. We love candidates who can troubleshoot and debug effectively, so let us know how you’ve ensured reliability and performance in your past projects.
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 gives you a chance to explore more about what we do at StudySmarter!
How to prepare for a job interview at Stott and May
✨Know Your Azure Inside Out
Make sure you brush up on your Azure Data services knowledge, especially Azure Data Factory and Databricks. Be ready to discuss specific projects where you've designed and built data pipelines, as well as any challenges you faced and how you overcame them.
✨Showcase Your Problem-Solving Skills
Prepare to talk about times when you've troubleshot and debugged data pipelines. Think of examples that highlight your analytical skills and how you ensured reliability and performance in your previous roles.
✨Communicate Clearly with Stakeholders
Since collaboration is key, practice explaining complex technical concepts in simple terms. Be ready to share how you've worked with stakeholders to gather data requirements and translate them into effective solutions.
✨Demonstrate Your Data Governance Knowledge
Familiarise yourself with data quality, governance, and security standards. Be prepared to discuss how you've maintained these standards in past projects, particularly in relation to large and complex datasets.