Azure Data Engineer: Databricks & Cloud Pipelines (Hybrid) in London

Azure Data Engineer: Databricks & Cloud Pipelines (Hybrid) in London

London Temporary 45000 - 50000 £ / year (est.) Home office (partial)
Jefferson Frank

At a Glance

  • Tasks: Design and optimise data pipelines in Azure, working on ETL/ELT processes.
  • Company: Join Jefferson Frank, a leader in tech recruitment with a focus on innovation.
  • Benefits: Hybrid work model, immediate start, and potential for contract extension.
  • Why this job: Be at the forefront of cloud technology and make a real impact on data solutions.
  • Qualifications: Experience with Azure and data engineering principles required.

The predicted salary is between 45000 - 50000 £ per year.

Jefferson Frank is seeking an Azure Data Engineer to design, develop and optimise data pipelines in Azure.

You will work closely with architects and analysts to deliver a modern, scalable data platform.

Key responsibilities include building ETL/ELT processes, migrating legacy workloads to the cloud and maintaining data governance.

This 6-month hybrid contract offers an immediate start and potential extension.

#J-18808-Ljbffr

Azure Data Engineer: Databricks & Cloud Pipelines (Hybrid) in London employer: Jefferson Frank

Join a leading UK manufacturing and supply-chain organisation that is at the forefront of digital transformation. With a strong commitment to employee development, you will have access to competitive salaries and opportunities to enhance your IT and Data management skills in a collaborative and dynamic work environment. Located in Surrey, this hybrid role offers the chance to engage with diverse stakeholders while contributing to innovative solutions that drive business success.

Jefferson Frank

Contact Details:

Jefferson Frank Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Azure Data Engineer: Databricks & Cloud Pipelines (Hybrid) in London

Tap into Online Data Science Communities

Join online communities focused on data science like Kaggle, LinkedIn groups, or Reddit threads. These are goldmines for temporary gigs, as you can network with professionals and potentially hear about opportunities at companies like Jefferson Frank before they're even advertised!

Show Off Your Skills With Projects

Got some cool data science projects? Showcase them on platforms like GitHub or create a personal portfolio website. This visibility is crucial for landing temporary roles—let recruiters see your actual skills in action, which can set you apart from the crowd.

Check Out Specialist Job Boards

For temp roles, hit up job boards dedicated to tech and data science, like Stack Overflow Jobs or DataJobs. These platforms often feature openings that you won’t find on general job sites, including contracts with companies like Jefferson Frank.

Leverage University Resources

If you're currently at uni or recently graduated, tap into your school's career services. They often have connections with companies looking for temporary data science interns or contract workers, and they might even host job fairs with employers like Jefferson Frank.

We think you need these skills to ace Azure Data Engineer: Databricks & Cloud Pipelines (Hybrid) in London

SQL
Python
Data Pipeline Development
Data Engineering
API Integration
Problem-Solving Skills
ETL/ELT Processes

Some tips for your application 🫡

Highlight Your Data Projects:When applying for a temporary data science role at Jefferson Frank, make sure to showcase any relevant projects you've worked on. Whether it's a personal project, an academic undertaking, or contributions to an open-source initiative, detailing these experiences can really set you apart and demonstrate your practical skills.

Emphasise Your Analytical Skills:In your CV and cover letter, focus on the specific analytical skills that are key to data science. Mention any experience with statistical tools, programming languages like Python or R, and data visualisation software. Don't forget to include any certifications that may bolster your expertise!

Show Your Flexibility:Since this is a temporary role, it's important to convey your adaptability and willingness to learn. In your cover letter to Jefferson Frank, emphasise how quickly you can get up to speed with new tools or projects. Highlight any previous experiences where you've had to adjust to new environments or challenges.

Craft a Unique Data-Driven Cover Letter:Instead of the usual generic cover letter, spice it up with some data! Maybe you’ve improved a process by 20% in a past role or cleaned a dataset with over a million entries. Use these stats to your advantage to grab Jefferson Frank’s attention and show the tangible impact of your work.

How to prepare for a job interview at Jefferson Frank

Showcase Your Analytical Skills

For a data science gig, it's crucial to demonstrate your analytical abilities. Be ready to discuss previous projects and the methodologies you used. Think about how you can quantify your impact—did your analysis improve efficiency or save costs? These are the stories that will stick with interviewers at Jefferson Frank.

Brush Up on Technical Skills

You might face technical questions on tools relevant to data science, like Python, R, or SQL. Prepare to solve a problem live—perhaps they'll ask you to write a simple query or code snippet. It’s cool to talk about them, but we need to show we can do it in practice, especially in a temporary role where quick results matter.

Highlight Your Adaptability

Since this is a temporary position, emphasise your ability to learn quickly and adapt to new tools or workflows. Share examples of how you've thrived in fast-paced environments before, and how you can hit the ground running at Jefferson Frank.

Prepare a Portfolio of Your Work

Bring your portfolio to the table—showcase projects where you've leveraged data science techniques to solve problems. Whether it’s a GitHub repository or a set of case studies, having tangible examples of your work will help you stand out and show what you bring to the team at Jefferson Frank.