At a Glance
- Tasks: Lead data engineering projects, designing and building Azure + Databricks pipelines for AI insights.
- Company: Join a fast-growing enterprise programme with a focus on innovation and collaboration.
- Benefits: Competitive salary, flexible hours, and opportunities for professional growth.
- Other info: Opportunity to coach mid-level engineers and lead design reviews in a supportive team.
- Why this job: Make an impact by working on cutting-edge AI-driven data products in a dynamic environment.
- Qualifications: 8+ years in data engineering, expert in Azure Databricks and strong Python + SQL skills.
The predicted salary is between 70000 - 90000 € per year.
You will lead the data-engineering thread across a fast-growing, multi-project enterprise programme. You will design, build and own production-grade Azure + Databricks pipelines that feed AI-powered insights, natural-language querying and downstream CRM and operational activation — and you will do it client-facing, in UK hours, alongside a small, senior team.
Architecture leadership:
- Define and document target architectures across Azure + Databricks for new and in-flight projects.
- Build production ingestion → bronze/silver/gold (medallion) → curated data products using ADF, PySpark, Delta Lake and Databricks Workflows / DLT.
AI-grounded data products:
- Partner with the GenAI engineer to expose Gold data to RAG agents, Azure AI Search and Azure OpenAI — including chunking, embeddings, citations and evals.
Use Databricks Asset Bundles, Terraform/Bicep and Azure DevOps Pipelines to make every change reviewable, testable and roll-back-able. Lead design reviews, status calls and stakeholder workshops in UK hours; Coach mid-level engineers, set patterns, run code reviews and quality bars across the data team.
Requirements:
- 8+ years in data engineering, with 4+ years on Azure data services in production.
- Expert in Azure Databricks — Delta Lake, PySpark, SQL, Workflows / Jobs, cluster tuning, MLflow basics.
- Strong with Azure Data Factory (or Synapse Pipelines), ADLS Gen2, Azure SQL, Key Vault.
- Hands-on with at least one IaC tool (Terraform or Bicep) and CI/CD via Azure DevOps or GitHub Actions; Databricks Asset Bundles a plus.
- Working knowledge of Azure AI Search and Azure OpenAI — including RAG patterns, embeddings, chunking strategies and grounded prompting.
- Comfortable with Unity Catalog, role-based access and data governance practices (Purview, sensitivity labels).
- Strong Python + SQL; Excellent written English.
Azure AI DataBricks Engineer employer: Neev
Join a dynamic and innovative team as an Azure AI DataBricks Engineer, where you'll have the opportunity to lead cutting-edge data engineering projects in a collaborative environment. Our company fosters a culture of continuous learning and professional growth, offering mentorship from senior engineers and the chance to work on impactful AI-driven solutions. Located in the UK, we provide a flexible work schedule that aligns with local hours, ensuring a healthy work-life balance while you contribute to exciting enterprise programmes.
StudySmarter Expert Advice🤫
We think this is how you could land Azure AI DataBricks Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in your industry on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can get you in the door.
✨Tip Number 2
Prepare for those interviews by practising common questions and scenarios related to Azure and Databricks. We recommend doing mock interviews with friends or using online platforms to boost your confidence.
✨Tip Number 3
Showcase your skills! Create a portfolio of projects that highlight your experience with Azure, Databricks, and data engineering. We love seeing real-world applications of your expertise!
✨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’re always on the lookout for talented individuals like you!
We think you need these skills to ace Azure AI DataBricks Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Azure AI DataBricks Engineer role. Highlight your experience with Azure, Databricks, and any relevant projects you've worked on. We want to see how your skills match what we're looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about data engineering and how you can contribute to our team. Be sure to mention specific technologies like PySpark and Delta Lake that you've worked with.
Showcase Your Projects:If you've got any projects that demonstrate your expertise in building data pipelines or using Azure services, make sure to include them. We love seeing real-world applications of your skills, so don’t hold back!
Apply Through Our Website:We encourage you to apply through our website for the best chance of getting noticed. It’s super easy, and we’ll be able to review your application more efficiently. Plus, it shows you’re serious about joining us at StudySmarter!
How to prepare for a job interview at Neev
✨Know Your Tech Inside Out
Make sure you’re well-versed in Azure Databricks, Delta Lake, and PySpark. Brush up on your SQL skills too! Be ready to discuss specific projects where you've used these technologies, as this will show your hands-on experience.
✨Showcase Your Leadership Skills
Since the role involves leading design reviews and coaching mid-level engineers, prepare examples of how you've successfully led teams or projects in the past. Highlight your ability to set patterns and maintain quality standards across a data team.
✨Understand the Client-Facing Aspect
This position requires working closely with clients during UK hours. Think about how you can demonstrate your communication skills and ability to manage stakeholder expectations. Prepare to discuss any previous client-facing experiences.
✨Be Ready for Technical Challenges
Expect technical questions or even a practical test related to building Azure + Databricks pipelines. Familiarise yourself with the medallion architecture and be prepared to explain how you would approach building production ingestion pipelines.
