At a Glance
- Tasks: Design and build data pipelines on AWS for machine learning and real-time decision making.
- Company: Exciting UK tech scale-up focused on privacy-first digital marketing solutions.
- Benefits: Competitive salary, hybrid work model, and clear progression opportunities.
- Other info: Join a dynamic team with greenfield projects and high ownership.
- Why this job: Shape modern data infrastructure and make a real impact in a growing SaaS business.
- Qualifications: Experience with Python, SQL, and AWS data services; collaboration skills are a must.
The predicted salary is between 70000 - 90000 € per year.
This is an opportunity to join a scaling SaaS business where data sits at the heart of the product. You will play a key role in shaping modern data infrastructure that directly supports machine learning systems, real time decision making, and measurable commercial outcomes. The role offers high ownership, greenfield projects, and the chance to influence how data is used across the organisation as it continues to grow.
The Company
They are a UK based technology scale up building a privacy first, cookieless platform that helps businesses protect and optimise their digital marketing spend. Using machine learning and large scale behavioural data, they analyse vast volumes of traffic in real time to identify low quality or invalid activity. With offices in London and Manchester, they operate at Series A stage with strong funding and a collaborative, engineering led culture.
The Role
You will sit within the Data and Platform function, working closely with Data Science, Engineering, and Product teams to design and run reliable, scalable data systems. Key responsibilities include:
- Designing and owning batch and streaming data ingestion pipelines on AWS
- Building and maintaining ML ready datasets to support model training, inference, and experimentation
- Improving data warehouse design and performance within AWS Redshift, including refactoring poorly structured data
- Integrating new and underused data sources to unlock additional value
- Supporting feature store development and data pipelines for A/B testing and analytics tools
- Optimising data systems for cost, performance, reliability, and data freshness
- Contributing to greenfield initiatives while scaling existing data infrastructure handling very high volumes of event data
Your Skills and Experience
- Strong commercial experience building production grade data pipelines using Python and SQL
- Hands on experience with AWS data services such as S3, Redshift, Glue, Athena, and streaming technologies like Kinesis
- Experience working with large scale, high velocity event data and understanding the trade offs around cost, performance, and reliability
- Ability to think beyond implementation and understand how data supports business and product outcomes
- Comfortable collaborating across Data, Engineering, and Product in a fast moving environment
- Exposure to ML or analytics use cases, including preparing data for modelling or experimentation, is highly beneficial
What They Offer
Clear scope for progression as the data platform and team continue to scale.
How to Apply
If you are interested in building high impact data systems in a growing SaaS environment, apply now to find out more about this opportunity.
AWS Data Engineer in Manchester employer: LinkedIn
Join a dynamic and innovative technology scale-up in Manchester, where you will have the opportunity to shape cutting-edge data infrastructure that drives impactful machine learning systems. With a strong focus on employee growth, a collaborative engineering culture, and the chance to work on greenfield projects, this role offers a unique environment for those looking to make a significant contribution while enjoying the benefits of a hybrid working model.
StudySmarter Expert Advice🤫
We think this is how you could land AWS Data Engineer in Manchester
✨Tip Number 1
Network like a pro! Reach out to people in the industry, especially those working at companies you're interested in. A friendly chat can open doors and give you insights that job descriptions just can't.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your data projects. This is your chance to demonstrate your expertise in building data pipelines and using AWS services like S3 and Redshift.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Be ready to discuss how you've tackled challenges in past projects, especially around data ingestion and machine learning.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace AWS Data Engineer in Manchester
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experience mentioned in the job description. Highlight your experience with AWS services, Python, and SQL, as these are key for the role. We want to see how your background aligns with what we're looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're excited about this opportunity and how you can contribute to our data infrastructure. Be genuine and let your personality come through – we love to see passion!
Showcase Relevant Projects:If you've worked on any projects that involved building data pipelines or using machine learning, make sure to mention them. We’re interested in seeing how you’ve tackled challenges and what impact your work had on previous teams or businesses.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us that you’re keen on joining our team at StudySmarter!
How to prepare for a job interview at LinkedIn
✨Know Your AWS Inside Out
Make sure you brush up on your knowledge of AWS data services like S3, Redshift, and Glue. Be ready to discuss how you've used these tools in past projects, especially in building data pipelines. The more specific examples you can provide, the better!
✨Showcase Your Python and SQL Skills
Prepare to demonstrate your coding skills in Python and SQL during the interview. You might be asked to solve a problem or optimise a query on the spot, so practice common data manipulation tasks beforehand. This will show that you're not just familiar with the languages but can also apply them effectively.
✨Understand the Business Impact of Data
Be ready to discuss how data influences business decisions and product outcomes. Think about examples where your work has directly contributed to measurable results. This shows that you understand the bigger picture and can align your technical skills with the company's goals.
✨Collaborate and Communicate
Since the role involves working closely with Data Science, Engineering, and Product teams, highlight your collaboration skills. Prepare examples of how you've successfully worked in cross-functional teams and how you communicate complex data concepts to non-technical stakeholders.
