At a Glance
- Tasks: Build scalable data pipelines and optimise Lakehouse architectures on the Databricks platform.
- Company: Join NTT DATA, a leader in tech innovation and diversity.
- Benefits: Competitive salary, diverse workplace, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on innovation and client satisfaction.
- Why this job: Make a real impact with cutting-edge data solutions and advanced analytics.
- Qualifications: 5-8 years in data engineering with strong Databricks expertise.
The predicted salary is between 81000 - 99000 £ per year.
Make an impact with NTT DATA Join a company that is pushing the boundaries of what is possible.
We are renowned for our technical excellence and leading innovations, and for making a difference to our clients and society.
Our workplace embraces diversity and inclusion – it’s a place where you can grow, belong and thrive.
Your day at NTT DATA
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 optimizing modern data ecosystems that enable data-driven decision making, advanced analytics, and AI capabilities for our clients.
As a trusted practitioner, you will design and implement robust ETL/ELT workflows, integrate real-time and batch data sources, and enable secure, well-governed data products and pipelines.
You will thrive in a collaborative, client-facing environment, with a passion for solving complex data challenges, driving innovation and ensuring the seamless delivery of data solutions.
- Primary Responsibilities
- Client Engagement & Delivery
- Data Pipeline Development (Batch and Streaming)
- Databricks & Lakehouse Architectures
- Data Modelling & Optimisation (Delta Lake, Medallion architecture)
- Collaboration & Best Practices
- 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
- Key Competencies
- 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/Py Spark/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 (Git Hub, Bitbucket) and CI/CD pipelines.
- Understanding of data governance, security, and compliance frameworks.
- Exposure to AI/ML workloads desirable.
Experience, 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.
- Preferred: BSc/MSc in Computer Science, Data Engineering, or related field
- 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.
- About NTT DATA
NTT DATA is a $30+ billion business and technology services leader, serving 75% of the Fortune Global 100.
We are committed to accelerating client success and positively impacting society through responsible innovation.
We are one of the world’s leading AI and digital infrastructure providers, with unmatched capabilities in enterprise-scale AI, cloud, security, connectivity, data centers and application services.
Our consulting and industry solutions help organizations and society move confidently and sustainably into the digital future.
As a Global Top Employer, we have experts in more than 70 countries.
We also offer clients access to a robust ecosystem of innovation centers as well as established and start-up partners.
NTT DATA is part of NTT Group, which invests over $3 billion each year in R&D.
Equal Opportunity Employer
NTT DATA is proud to be an Equal Opportunity Employer with a global culture that embraces diversity.
We are committed to providing an environment free of unfair discrimination and harassment.
We do not discriminate based on age, race, colour, gender, sexual orientation, religion, nationality, disability, pregnancy, marital status, veteran status, or any other protected category.
Join our growing global team and accelerate your career with us.
#J-18808-Ljbffr
Databricks Data Engineer in London employer: JobCubby
At Nearform, we pride ourselves on being an exceptional employer that fosters a collaborative and innovative work culture. As a Senior Golang Engineer, you will not only have the opportunity to work on high-impact projects with a global client portfolio but also benefit from continuous professional development and a supportive remote environment that values your contributions and growth.
StudySmarter Expert Advice🤫
We think this is how you could land Databricks Data Engineer in London
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like JobCubby!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Databricks Data Engineer at JobCubby.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like JobCubby.
✨Apply Directly through Our Website
When you find a suitable opening like Databricks Data Engineer at JobCubby, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Databricks Data Engineer in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at JobCubby, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at JobCubby. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at JobCubby
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at JobCubby!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
