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- Tasks: Lead initiatives to enhance data quality and reliability across the organisation.
- Company: Values-led organisation with a strong social purpose and collaborative culture.
- Benefits: Inclusive environment, opportunities for meaningful improvements, and support for professional growth.
- Why this job: Make a real impact on data quality and help shape a positive data culture.
- Qualifications: Experience in data governance and ability to engage diverse stakeholders.
- Other info: Join a team that values collaboration and focuses on long-term solutions.
The predicted salary is between 36000 - 60000 £ per year.
This is a genuinely influential role for someone who wants to improve data quality in a way that actually sticks. The Data Quality Improvement Manager sits within a central Technology function, reporting into a Head of Data Governance, and plays a key role in improving the quality, integrity and reliability of critical data across a large, complex organisation.
This isn’t about writing policies and walking away. It’s about working with the business, understanding how data is created and used, and helping teams improve it at source building trust in data over time.
What you’ll be doing:
- Leading organisation-wide initiatives to improve the quality, consistency and reliability of priority datasets
- Defining, embedding and maintaining data quality standards aligned to a wider data governance framework
- Working closely with Data Owners, Data Stewards, Technology teams and business stakeholders to drive accountability for data
- Helping non-technical teams understand why data quality matters and how to improve it in practice
- Identifying data risks and issues, escalating them appropriately and supporting effective resolution
- Contributing to a collaborative data culture where ownership and quality are shared responsibilities
About you:
You care about Data Culture, care about getting people on board. You’re someone who can bridge governance and delivery. You’ll have experience working with data governance and data quality in a complex or multi-entity organisation, and you’re confident engaging with a wide range of stakeholders from technical teams to senior business leaders. You’re pragmatic, structured and collaborative. You enjoy untangling messy data problems, prioritising what matters most, and helping organisations improve how data is managed day-to-day.
Why this opportunity:
You’ll be joining a values-led organisation with a strong social purpose, where data is recognised as a critical enabler of good decision-making and service delivery. The culture is collaborative, inclusive and outcomes-focused. You’ll be trusted to do your job properly, encouraged to challenge constructively, and supported to make meaningful improvements rather than short-term fixes.
Data Quality Manager in Worcester employer: SF Technology Solutions
Contact Detail:
SF Technology Solutions Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Quality Manager in Worcester
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend events, and connect with potential colleagues on LinkedIn. Building relationships can open doors that a CV just can't.
✨Tip Number 2
Prepare for interviews by understanding the company’s data culture. Research their values and think about how your experience aligns with their goals. This shows you’re genuinely interested and ready to contribute.
✨Tip Number 3
Practice your storytelling skills! Be ready to share specific examples of how you've improved data quality in past roles. This helps interviewers see the real impact you can make.
✨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 joining our team.
We think you need these skills to ace Data Quality Manager in Worcester
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter for the Data Quality Manager role. Highlight your experience with data governance and quality, and show us how you've made a real impact in previous roles.
Show Your Passion for Data Culture: We want to see your enthusiasm for building a strong data culture! Share examples of how you've engaged teams in understanding the importance of data quality and how you've helped them improve their practices.
Be Clear and Concise: When writing your application, keep it straightforward. Use clear language and avoid jargon where possible. We appreciate a well-structured application that gets straight to the point!
Apply Through Our Website: Don’t forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for this exciting opportunity.
How to prepare for a job interview at SF Technology Solutions
✨Know Your Data Inside Out
Before the interview, dive deep into the data quality principles and practices relevant to the role. Familiarise yourself with common data governance frameworks and be ready to discuss how you've applied these in past roles. This shows you’re not just a theoretical expert but someone who can bring practical insights.
✨Showcase Your Collaborative Spirit
This role is all about working with various teams, so be prepared to share examples of how you've successfully collaborated with non-technical stakeholders. Highlight instances where you’ve helped others understand the importance of data quality and how you’ve driven accountability across teams.
✨Prepare for Scenario-Based Questions
Expect questions that ask you to solve hypothetical data quality issues. Think through potential scenarios where data integrity might be compromised and how you would address them. This will demonstrate your problem-solving skills and your ability to think on your feet.
✨Emphasise Your Pragmatic Approach
The interviewers will want to see that you can balance ideal solutions with practical implementation. Be ready to discuss how you prioritise data quality initiatives and manage risks effectively. Share specific examples of how you’ve tackled messy data problems in a structured way.
