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For Companies At a Glance
- Tasks: Transform messy data into clean, reliable datasets for teams and clients.
- Company: Join a forward-thinking company focused on data innovation.
- Benefits: Flexible working hours, hybrid role, and a full home-working setup.
- Why this job: Make a real impact by shaping data products and collaborating with diverse teams.
- Qualifications: Strong SQL skills, experience with Python, and a keen eye for detail.
- Other info: Great career growth opportunities and a chance to work with cutting-edge AI tools.
The predicted salary is between 28800 - 48000 £ per year.
Not every data role is about dashboards or ad-hoc analysis. This one is for someone who enjoys getting close to the data itself, taking messy, real-world raw data and turning it into clean, reliable datasets that other teams and clients can actually trust and use. It's a hands-on role sitting at the intersection of data modelling, quality and product thinking, with plenty of ownership and room to influence how data products are designed and evolved.
What you’ll be working on
You’ll be part of a Data Products team responsible for shaping behavioural data into well-defined, client-ready datasets. Day to day, you will:
- Design and evolve data schemas and fields, turning product requirements into clear, well modelled datasets
- Build and maintain data feeds using SQL, Python and internal (AI-assisted) tooling
- Apply business logic, validation rules and quality checks across large datasets
- Investigate data issues and improve reliability, consistency and trust in the outputs
- Work closely with Product, Data Engineering, Apps and ML teams to deliver new features and improvements
- Keep documentation clear, current and genuinely useful
This is a role for someone who cares about how data is structured, named and validated, not just whether a query runs.
Who this role suits
This role is a good fit if you:
- Enjoy working hands on with data rather than sitting at arm's length from it
- Like figuring out how real-world digital behaviour should be represented cleanly
- Care about data quality, edge cases and consistency
- Are comfortable collaborating with engineers, product managers and non-technical stakeholders
- Are open to using AI tools to speed up understanding and reduce repetitive work
You’ll likely bring:
- Strong SQL skills and experience working with large datasets
- Experience with at least one data-friendly language (such as Python)
- A high level of attention to detail
- Clear communication skills and a collaborative mindset
Nice to have (but not essential):
- Experience with event-level or behavioural data (web, apps, ads, etc.)
- Awareness of privacy and governance considerations
- Familiarity with AWS-based data stacks (S3, Spark/EMR, Athena, Airflow, notebooks)
How you’ll work
- Hybrid role, Manchester-based
- 2 days per week in the office, the rest flexible
- Flexible start and finish times
- Full home-working setup provided
Data Products Engineer in Manchester employer: Circle Group
Contact Detail:
Circle Group Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Products Engineer in Manchester
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data projects, especially those involving SQL and Python. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on common data-related questions and scenarios. Practice explaining your thought process when working with messy datasets and how you ensure data quality. Confidence is key!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to get noticed and ensures your application lands directly in the right hands. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Data Products Engineer in Manchester
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that match the Data Products Engineer role. Highlight your SQL and Python expertise, and any hands-on data work you've done. We want to see how you can turn messy data into something reliable!
Showcase Your Projects: If you've worked on any relevant projects, whether in a job or during your studies, include them! Describe how you approached data modelling and quality checks. This gives us a glimpse of your problem-solving skills and attention to detail.
Keep It Clear and Concise: When writing your application, clarity is key. Use straightforward language and avoid jargon unless it's relevant. We appreciate a well-structured application that gets straight to the point—just like we do with our data!
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’re considered for the role. Plus, it shows us you're keen to join the StudySmarter team!
How to prepare for a job interview at Circle Group
✨Know Your Data Tools
Make sure you brush up on your SQL and Python skills before the interview. Be ready to discuss how you've used these tools in past projects, especially when it comes to cleaning and structuring messy datasets.
✨Showcase Your Problem-Solving Skills
Prepare examples of how you've tackled data issues in the past. Think about specific challenges you faced with data quality or consistency and how you resolved them. This will demonstrate your hands-on approach and attention to detail.
✨Understand the Business Context
Familiarise yourself with how data products impact business decisions. Be prepared to discuss how you would apply business logic and validation rules to ensure the datasets are reliable and useful for clients.
✨Communicate Clearly
Since this role involves collaboration with various teams, practice explaining complex data concepts in simple terms. Clear communication is key, so think about how you can convey your ideas effectively to both technical and non-technical stakeholders.
