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For Companies At a Glance
- Tasks: Build scalable data pipelines and optimise Lakehouse architectures on the Databricks platform.
- Company: Join a forward-thinking Data & AI practice with a focus on innovation.
- Benefits: Flexible work options, tailored benefits, and continuous learning opportunities.
- Other info: Diverse and inclusive workplace committed to equity and employee wellbeing.
- Why this job: Make a real impact by enabling advanced analytics and AI for clients.
- Qualifications: 5-8 years in data engineering with strong Databricks expertise required.
The predicted salary is between 60000 - 80000 £ per year.
We are seeking a highly skilled Databricks Data Engineer to join our Data & AI practice. The successful candidate will have deep expertise in building scalable data pipelines, optimizing Lakehouse architectures and enabling advanced analytics and AI use cases on the Databricks platform. This role is critical in building and optimising modern data ecosystems that enable data-driven decision making, advanced analytics, and AI capabilities for our clients.
What you’ll be doing
- Client Engagement & Delivery
- Data Pipeline Development (Batch and Streaming)
- Databricks & Lakehouse Architectures
- Quality, Governance & Security
Business Relationships
- Solution Architects
- Data Engineers, Developers, ML Engineers, and Analysts
- Client stakeholders up to Head of Data Engineering, Chief Data Architect, and Analytics leadership
What experience you’ll bring
- Proven experience in data engineering and pipeline development on Databricks and cloud-native platforms.
- Strong consulting values with ability to collaborate effectively in client-facing environments.
- Hands‑on expertise across the data lifecycle: ingestion, transformation, modelling, governance, and consumption.
- Strong problem‑solving, analytical, and communication skills.
- Experience leading or mentoring teams of engineers to deliver high‑quality scalable data solutions.
Technical Expertise
- Deep expertise with the Databricks platform (Spark/PySpark/Scala, Delta Lake, Unity Catalog, MLflow).
- Proficiency in ETL/ELT tools such as DBT, Matillion, Talend, or equivalent.
- Strong SQL and Python (or equivalent language) skills for data manipulation and automation.
- Hands‑on experience with cloud platforms (AWS, Azure, GCP).
- Familiarity with Databricks Workflows and other orchestration tools.
- Knowledge of data modelling methodologies (star schemas, Data Vault, Kimball, Inmon).
- Familiarity with medallion architectures, data lakehouse principles and distributed data processing.
- Experience with version control tools (GitHub, Bitbucket) and CI/CD pipelines.
- Understanding of data governance, security, and compliance frameworks.
- Exposure to AI/ML workloads desirable.
Qualifications and Education
- Experience: Minimum 5–8 years in data engineering, data warehousing, or data architecture roles, with at least 3+ years working with Databricks.
- Education: University degree required (BSc/MSc in Computer Science, Data Engineering, or related field preferred).
- Databricks certifications (Data Engineer Professional) highly desirable.
Measures of Success
- Delivery of high‑performing, scalable, and secure data pipelines aligned to client requirements.
- High client satisfaction and successful adoption of Databricks‑based solutions.
- Demonstrated ability to innovate and improve data engineering practices.
- Contribution to the growth of the practice through reusable assets, accelerators, and technical leadership.
What we’ll offer you
We offer a range of tailored benefits that support your physical, emotional, and financial wellbeing. Our Learning and Development team ensures continuous growth and development opportunities for our people. We also offer flexible work options. We are an equal opportunities employer. We believe in the fair treatment of all our employees and commit to promoting equity and diversity in our employment practices. We are a proud Disability Confident & Committed Employer and commit to creating a diverse and inclusive workforce.
Databricks Data Engineer employer: NTT DATA
Contact Detail:
NTT DATA Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Databricks Data Engineer
✨Network Like a Pro
Get out there and connect with folks in the industry! Attend meetups, webinars, or even local tech events. The more people you know, the better your chances of landing that Databricks Data Engineer role.
✨Show Off Your Skills
Create a portfolio showcasing your data pipeline projects and any cool stuff you've built on Databricks. This is your chance to shine and demonstrate your hands-on expertise to potential employers.
✨Ace the Interview
Prepare for technical interviews by brushing up on your SQL, Python, and Databricks knowledge. Practice common data engineering scenarios and be ready to discuss how you've tackled challenges in past projects.
✨Apply Through Our Website
Don't forget to apply directly through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about joining our team.
We think you need these skills to ace Databricks Data Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Databricks and data engineering. Use keywords from the job description to show we’re on the same page about what you bring to the table.
Showcase Your Projects: Include specific examples of data pipelines you've built or optimised. We love seeing real-world applications of your skills, especially if they relate to advanced analytics or AI use cases.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Explain why you’re excited about the role and how your background aligns with our needs. Keep it conversational but professional – we want to get to know you!
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 NTT DATA
✨Know Your Databricks Inside Out
Make sure you brush up on your Databricks knowledge before the interview. Be ready to discuss your hands-on experience with Spark, Delta Lake, and MLflow. Prepare examples of how you've built scalable data pipelines and optimised Lakehouse architectures in previous roles.
✨Showcase Your Problem-Solving Skills
During the interview, be prepared to tackle some technical challenges or case studies. Think about how you can demonstrate your analytical skills and problem-solving abilities. Use real-life scenarios where you've successfully navigated complex data issues to highlight your expertise.
✨Communicate Effectively
Since this role involves client engagement, practice articulating your thoughts clearly and concisely. Be ready to explain technical concepts in a way that non-technical stakeholders can understand. Good communication can set you apart from other candidates.
✨Prepare Questions for Them
Interviews are a two-way street! Prepare insightful questions about their data engineering practices, team dynamics, and future projects. This shows your genuine interest in the role and helps you assess if the company is the right fit for you.