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- Tasks: Lead data quality initiatives and establish standards for core datasets in Asset Management.
- Company: Join a forward-thinking IT team in Manchester focused on data excellence.
- Benefits: Competitive salary, career growth opportunities, and a dynamic work environment.
- Other info: Be part of a newly formed team with the chance to shape processes from scratch.
- Why this job: Make a real impact by ensuring data quality and driving continuous improvement.
- Qualifications: Degree in relevant field and experience in data governance or management.
The predicted salary is between 60000 - 105000 £ per year.
We are seeking a highly skilled and experienced Data Quality Lead (DQL) to fill a foundational role in a newly formed group concentrating on Data. This role will work closely with the business and Data Stewards to define what ‘good’ data looks like. The DQL also partners with Data Engineering to implement frameworks that ensure data quality aligns to business standards. Ultimately, this is a key position that will drive a continuous improvement process that builds trust and confidence in our data.
About the role
- Establish domain specific data quality rules and dimension thresholds (accuracy, completeness, consistency, validity, timeliness) for core Asset Management datasets spanning Real Estate, Lending & Credit.
- Define and establish metrics to assess data quality performance and drive continuous improvement.
- Partner with Data Engineering to design automated checks, dashboards, and alerts for pipelines, data products, and key reports.
- Proactively lead root-cause analysis of data incidents.
- Coordinate fixes with source platforms.
- Use trend analysis to prioritize remediation, reduce chronic/recurring defects, and improve overall data reliability and trust.
- Maintain expertise in data quality industry trends and strategically implement advanced methodologies to elevate data quality practices.
About you
- Degree in Computer Science, Information Systems, Data Science, or a related field.
- Demonstrated experience in a Data Governance or Data Management program.
- Proven experience leading data quality, data governance, or data controls in a data rich environment (financial services, credit, real estate, asset management, or similar).
- A background in financial data domains (IBOR/ABOR, transactions, market data, reference data).
- Experience working with business stakeholders to define critical data elements, data definitions, and “fit for use” requirements.
- Familiarity with data quality tooling and modern orchestration/observability practices.
- Comfortable building processes from scratch in a newly formed team.
- Resourceful, motivated self-starter with the ability to collaborate across business and technology.
- Strong analytical, verbal, and written communication skills.
Data Quality Lead in Manchester employer: Arrow
Contact Detail:
Arrow Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Quality Lead in Manchester
✨Tip Number 1
Network like a pro! Reach out to your connections in the data field, attend industry meetups, and engage on platforms like LinkedIn. We all know that sometimes it’s not just what you know, but who you know that can help you land that Data Quality Lead role.
✨Tip Number 2
Show off your skills! Create a portfolio or a personal project that highlights your expertise in data quality and governance. This is a great way for us to demonstrate our capabilities beyond the CV and catch the eye of potential employers.
✨Tip Number 3
Prepare for interviews by brushing up on common data quality scenarios and challenges. We should be ready to discuss how we’ve tackled issues in the past and how we can bring that experience to the new team. Practice makes perfect!
✨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 often have exclusive roles listed there that you won’t find anywhere else.
We think you need these skills to ace Data Quality Lead in Manchester
Some tips for your application 🫡
Tailor Your CV: Make sure your CV speaks directly to the Data Quality Lead role. Highlight your experience in data governance and management, especially in financial services or similar fields. We want to see how your skills align 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 passionate about data quality and how you can contribute to our team. Don’t forget to mention any specific projects or achievements that showcase your expertise.
Showcase Your Analytical Skills: In your application, be sure to highlight your analytical abilities. We’re looking for someone who can lead root-cause analysis and drive continuous improvement, so share examples of how you’ve done this in the past.
Apply Through Our Website: We encourage you to apply through our website for the best chance of getting noticed. It’s super easy, and you’ll be able to keep track of your application status. Plus, we love seeing applications come directly from our site!
How to prepare for a job interview at Arrow
✨Know Your Data Quality Metrics
Before the interview, brush up on key data quality metrics like accuracy, completeness, and consistency. Be ready to discuss how you’ve used these metrics in past roles to drive improvements and build trust in data.
✨Showcase Your Collaboration Skills
This role requires working closely with various stakeholders. Prepare examples of how you've successfully collaborated with business teams and data engineers in the past. Highlight your ability to communicate complex data concepts in a way that everyone can understand.
✨Demonstrate Your Problem-Solving Abilities
Be prepared to discuss specific instances where you led root-cause analysis for data incidents. Share your thought process and the steps you took to resolve issues, as this will show your analytical skills and proactive approach.
✨Stay Updated on Industry Trends
Familiarise yourself with the latest trends in data quality and governance. Mention any advanced methodologies or tools you’ve implemented in previous roles, as this will demonstrate your commitment to continuous improvement and innovation in data practices.