Databricks Data Engineer β€” Lakehouse & AI Solutions in London

Databricks Data Engineer β€” Lakehouse & AI Solutions in London

London Full-Time 63000 - 77000 Β£ / year (est.) No working from home possible
J

At a Glance

  • Tasks: Design and implement scalable ETL/ELT workflows and build lakehouse architectures.
  • Company: Join NTT DATA, a leader in Data & AI solutions in the UK.
  • Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
  • Other info: Collaborative environment with a focus on delivering impactful data solutions.
  • Why this job: Work on exciting projects that leverage advanced analytics and AI technologies.
  • Qualifications: Experience in data engineering, governance, and cloud platforms required.

The predicted salary is between 63000 - 77000 Β£ per year.

NTT DATA is seeking a highly skilled Databricks Data Engineer to join our Data & AI practice in the UK.

You will design and implement scalable ETL/ELT workflows, build lakehouse architectures, and enable advanced analytics and AI use cases on Databricks for client projects.

The role requires deep experience in data engineering, data governance, and cloud platforms, with a collaborative, client-facing mindset and a track record of delivering robust data solutions.

#J-18808-Ljbffr

Databricks Data Engineer β€” Lakehouse & AI Solutions 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.

J

Contact Details:

JobCubby Recruitment Team

StudySmarter Expert Advice🀫

We think this is how you could land Databricks Data Engineer β€” Lakehouse & AI Solutions 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 β€” Lakehouse & AI Solutions 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 β€” Lakehouse & AI Solutions 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 β€” Lakehouse & AI Solutions in London

Python
SQL
Problem-Solving Skills
Communication Skills
Data Engineering
Data Pipeline Development
API Integration

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.