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- Tasks: Design and build end-to-end data pipelines using PySpark and Azure Data Platform.
- Company: Strategic AI partner with a focus on innovation and collaboration.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Why this job: Join a dynamic team and shape the future of data engineering with cutting-edge technologies.
- Qualifications: Experience in data governance, cloud-native architectures, and AI/ML workflows.
- Other info: Exciting environment with strong emphasis on teamwork and creative problem-solving.
The predicted salary is between 43200 - 72000 £ per year.
A strategic AI partner is seeking a Data Architect in London to design and build end-to-end data pipelines using PySpark, Databricks, and Azure Data Platform. This role requires hands-on development of data solutions and ensuring data quality across teams.
Ideal candidates will have exposure to data governance, cloud-native architectures, and familiarity with AI/ML workflows. The company values team collaboration and innovative thinking in data engineering.
Azure Data Architect - End-to-End Data Platforms employer: Fractal
Contact Detail:
Fractal Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Azure Data Architect - End-to-End Data Platforms
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can help you land that Azure Data Architect role.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects with PySpark, Databricks, and Azure Data Platform. We want to see your hands-on development work and how you ensure data quality across teams.
✨Tip Number 3
Prepare for those interviews! Brush up on data governance and cloud-native architectures. We recommend practising common interview questions related to AI/ML workflows to really impress your potential employers.
✨Tip Number 4
Apply through our website! We make it super easy for you to find and apply for roles like this one. Plus, it shows you’re genuinely interested in joining our team and contributing to innovative thinking in data engineering.
We think you need these skills to ace Azure Data Architect - End-to-End Data Platforms
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your experience with PySpark, Databricks, and Azure Data Platform in your application. We want to see how you've used these tools to build data pipelines and ensure data quality.
Be a Team Player: Since we value collaboration, share examples of how you've worked with teams in the past. Talk about your role in ensuring data governance and how you’ve contributed to cloud-native architectures.
Innovative Thinking is Key: Don’t shy away from showcasing your innovative ideas! If you’ve implemented any creative solutions in data engineering or AI/ML workflows, let us know. We love fresh perspectives!
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 get to know you better. Plus, it shows you’re keen on joining our team!
How to prepare for a job interview at Fractal
✨Know Your Tech Stack
Make sure you’re well-versed in PySpark, Databricks, and the Azure Data Platform. Brush up on your hands-on development skills and be ready to discuss how you've used these technologies in past projects. This will show that you can hit the ground running!
✨Showcase Your Data Governance Knowledge
Be prepared to talk about data quality and governance. Think of examples where you’ve ensured data integrity or implemented governance frameworks. This will demonstrate your understanding of the importance of data quality across teams.
✨Emphasise Collaboration
Since the company values team collaboration, come armed with stories that highlight your ability to work effectively in a team. Discuss how you’ve collaborated with others to solve complex data challenges or innovate solutions.
✨Think AI/ML Workflows
Familiarity with AI/ML workflows is key for this role. Prepare to discuss any experience you have with integrating data solutions into AI/ML processes. This will show that you understand the bigger picture and can contribute to innovative thinking in data engineering.
