Data Engineer: Cloud Pipelines & Analytics in Southampton

Data Engineer: Cloud Pipelines & Analytics in Southampton

Southampton Full-Time 53000 - 71000 £ / year (est.) Home office (partial)
Leonardo

At a Glance

  • Tasks: Design and maintain robust ETL/ELT pipelines for cloud-based data environments.
  • Company: Join Leonardo, a leader in Aerospace, Defence, and Security.
  • Benefits: Hybrid working, competitive salary, generous leave, and a pension scheme with up to 15% employer contribution.
  • Other info: Exciting opportunities for career growth in a dynamic environment.
  • Why this job: Make an impact in a cutting-edge industry while developing your data engineering skills.
  • Qualifications: Strong expertise in SQL, Python, and data warehousing required.

The predicted salary is between 53000 - 71000 £ per year.

Leonardo in Southampton is looking for a Data Engineer to design and maintain robust ETL/ELT pipelines and optimize cloud-based data environments. The role requires strong expertise in SQL, Python, and data warehousing.

This position offers a salary range of £53,000 - £71,000, hybrid working options, and comprehensive benefits including generous leave and a pension scheme with up to 15% employer contribution.

Join a leading organization in Aerospace, Defence, and Security.

Data Engineer: Cloud Pipelines & Analytics in Southampton employer: Leonardo

Leonardo in Southampton is an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration within the Aerospace, Defence, and Security sectors. Employees benefit from hybrid working options, a competitive salary range, and a comprehensive benefits package that includes generous leave and a pension scheme with up to 15% employer contribution, ensuring both personal and professional growth opportunities in a cutting-edge environment.

Leonardo

Contact Details:

Leonardo Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Data Engineer: Cloud Pipelines & Analytics in Southampton

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 Leonardo!

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 Data Engineer: Cloud Pipelines & Analytics at Leonardo.

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 Leonardo.

Apply Directly through Our Website

When you find a suitable opening like Data Engineer: Cloud Pipelines & Analytics at Leonardo, 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 Data Engineer: Cloud Pipelines & Analytics in Southampton

ETL
ELT
SQL
Python
Data Warehousing
Cloud-based Data Environments
Data Pipeline Design

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 Leonardo, 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 Leonardo. 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 Leonardo

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 Leonardo!

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.