At a Glance
- Tasks: Design and build data pipelines for real-time analytics in a fast-growing digital platform.
- Company: Join a rapidly scaling consumer platform focused on data-driven growth.
- Benefits: Competitive salary, remote work, and clear career progression opportunities.
- Other info: Work remotely with a dynamic team across the UK and Europe.
- Why this job: Make a real impact on a modern data platform and engineering culture.
- Qualifications: Strong experience in data engineering with Python, SQL, and AWS services.
The predicted salary is between 90000 - 100000 € per year.
This is an opportunity to join a fast scaling, digital first consumer platform at a pivotal point in its data journey. They are investing heavily in their data infrastructure and are building a high performing, software led data engineering team to support real time, high volume analytics across the business. You will have genuine influence over how data engineering is done, working on modern tooling and best practice from day one.
They are a UK based, consumer facing digital platform operating in a highly regulated online environment. Since launching less than a decade ago, the business has grown rapidly, supported by significant recent funding and a strong focus on product, user experience and responsible engagement. Data is central to their growth strategy, and they are building a modern, scalable analytics platform to support this ambition. The team operates fully remotely across the UK and Europe.
- Design, build and own end to end data pipelines supporting real time and batch analytics
- Work on high volume, low latency data use cases with a strong focus on reliability and scalability
- Develop production grade data solutions using software engineering best practices such as CI/CD, testing and version control
- Contribute to the design of data models and analytics layers used across the business
- Help implement and mature staging, testing and deployment processes across the data platform
- Collaborate closely with analytics, product and engineering teams to enable better decision making
Strong commercial experience as a data engineer in modern, cloud based environments:
- Advanced Python and SQL, with experience using PySpark or similar distributed processing frameworks
- Experience building and maintaining data pipelines in AWS using services such as S3, Glue or EKS
- Strong understanding of data modelling concepts and analytics best practice
- A software engineering mindset, including Git, testing and CI/CD workflows
The chance to help shape a growing data platform and engineering culture. Clear progression opportunities as the data team continues to expand. Exposure to complex, real world data challenges at scale.
If you are a Senior Data Engineer looking to make a real impact in a growing, product led business, apply now to learn more about the opportunity.
AWS Senior Data Engineer in Manchester employer: Harnham
Join a dynamic and rapidly growing digital platform that prioritises data at its core, offering you the chance to influence and shape the future of data engineering from day one. With a fully remote work culture across the UK and Europe, you'll enjoy flexibility, a strong focus on employee growth, and the opportunity to tackle complex data challenges in a supportive environment. Benefit from competitive remuneration and clear progression pathways as the company continues to expand its innovative data team.
StudySmarter Expert Advice🤫
We think this is how you could land AWS Senior Data Engineer in Manchester
✨Tip Number 1
Network like a pro! Reach out to current employees on LinkedIn or join relevant online communities. A friendly chat can give you insider info and might just get your foot in the door.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data engineering projects, especially those involving AWS, Python, and SQL. This will help you stand out and demonstrate your hands-on experience.
✨Tip Number 3
Prepare for the interview by brushing up on common data engineering challenges and solutions. Be ready to discuss how you've tackled real-time analytics and built reliable data pipelines in the past.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace AWS Senior Data Engineer in Manchester
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that match the job description. Highlight your experience with AWS, Python, and data pipelines to show us you’re the right fit for our team.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you’re passionate about data engineering and how you can contribute to our growing data platform. Be genuine and let your personality shine through!
Showcase Your Projects:If you've worked on relevant projects, don’t hesitate to mention them! We love seeing real-world applications of your skills, especially if they involve high volume, low latency data use cases.
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 our team!
How to prepare for a job interview at Harnham
✨Know Your Data Engineering Stuff
Make sure you brush up on your data engineering skills, especially in AWS. Be ready to discuss your experience with building and maintaining data pipelines using services like S3, Glue, or EKS. They’ll want to hear about specific projects where you’ve tackled high volume, low latency use cases.
✨Show Off Your Software Engineering Mindset
Since they’re looking for someone with a software engineering mindset, be prepared to talk about your experience with CI/CD, testing, and version control. Bring examples of how you've implemented these practices in your previous roles to demonstrate your commitment to quality and reliability.
✨Collaborate Like a Pro
This role involves working closely with analytics, product, and engineering teams. Think of examples where you’ve successfully collaborated across teams to enable better decision-making. Highlight your communication skills and how you’ve contributed to a team environment.
✨Be Ready to Discuss Data Modelling
They’ll want to know about your understanding of data modelling concepts and analytics best practices. Prepare to discuss how you’ve designed data models and analytics layers in the past, and be ready to share your thoughts on best practices in this area.
