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- Tasks: Design and maintain data systems while building data pipelines on AWS.
- Company: Harnham, a growing SaaS company in Manchester with a hybrid work model.
- Benefits: Clear career progression, competitive salary, and collaborative team environment.
- Other info: Exciting opportunity to grow in a fast-paced tech environment.
- Why this job: Join a dynamic team and work on real-time data and machine learning projects.
- Qualifications: Experience with Python, AWS services, and large-scale data processing required.
The predicted salary is between 50000 - 65000 £ per year.
Harnham is seeking a Data Engineer to join their team in Manchester (hybrid). In this role, you will design and maintain reliable data systems while collaborating closely with Data Science, Engineering, and Product teams.
Responsibilities include:
- Building data pipelines on AWS
- Optimizing data systems for performance and cost
The ideal candidate will have experience with Python, AWS services, and large-scale data processing. The role offers clear progression in a growing SaaS environment.
AWS Data Engineer – Real-Time Data & ML Pipelines in Manchester employer: Harnham
Contact Detail:
Harnham Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AWS Data Engineer – Real-Time Data & ML Pipelines in Manchester
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those working with AWS and data engineering. A friendly chat can lead to opportunities that aren’t even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving Python and AWS. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for technical interviews by brushing up on your data pipeline knowledge. Be ready to discuss how you've optimised systems in the past and how you would tackle real-time data challenges.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace AWS Data Engineer – Real-Time Data & ML Pipelines in Manchester
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with AWS, Python, and data processing. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re excited about the Data Engineer position and how you can contribute to our team. Let us know what makes you tick!
Showcase Your Projects: If you've worked on any cool data pipelines or ML projects, make sure to mention them in your application. We love seeing real-world examples of your work and how you tackle challenges.
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 us!
How to prepare for a job interview at Harnham
✨Know Your AWS Inside Out
Make sure you brush up on your knowledge of AWS services, especially those relevant to data engineering. Be prepared to discuss how you've used these services in past projects, and think about specific examples where you've optimised performance or reduced costs.
✨Showcase Your Python Skills
Since Python is a key part of the role, be ready to demonstrate your coding skills. You might be asked to solve a problem on the spot, so practice common data manipulation tasks and be familiar with libraries like Pandas and NumPy.
✨Understand Data Pipelines
Familiarise yourself with the concepts of data pipelines and real-time data processing. Be prepared to explain how you would design a pipeline for a specific use case, including considerations for reliability and scalability.
✨Collaborate and Communicate
This role involves working closely with various teams, so highlight your collaboration skills. Think of examples where you've successfully worked with data scientists or product teams, and be ready to discuss how you handle feedback and adapt to different perspectives.