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- Tasks: Ensure data quality and governance while collaborating with various departments.
- Company: Join a successful London Market syndicate focused on data integrity.
- Benefits: Enjoy a dynamic work environment with opportunities for professional growth.
- Why this job: Be part of a team that drives data-driven decision-making and enhances business impact.
- Qualifications: 3+ years in data governance; experience in regulated environments is essential.
- Other info: Insurance sector experience is required; this role is perfect for detail-oriented individuals.
The predicted salary is between 36000 - 60000 £ per year.
A successful and established London Market syndicate is seeking a highly motivated and detail-oriented Data Quality Analyst to join their team. The Data Quality Analyst is integral to ensuring the organization's data assets are managed, secured, and utilized to support business decision-making.
As Data Quality Analyst will play a key role in maintaining and driving quality standards, policies, and procedures related to data governance while collaborating with stakeholders across departments to enhance data quality and integrity.
NB: If you do not have experience of working in the insurance sector, you will unfortunately NOT be eligible for this role.
Key Responsibilities:
- Develop and implement a comprehensive data governance framework
- Help create, implement, and maintain data governance policies and procedures to ensure the business is compliant with relevant regulatory requirements
- Work with data owners and data stewards to establish and enforce data quality metrics, monitoring, and improvement processes
- Identify and appoint data stewards, providing guidance and support
- Develop and maintain a data catalog, taxonomy, and data lineage documentation
- Advocate for data quality best practices and drive initiatives to enhance data-driven decision-making
- Develop and deliver training and awareness programs to promote data governance & data quality principles across the organization
Experience Required:
- Minimum of 3 years in data governance, data management, or a related field
- Data Governance Expertise: Proven experience in developing and implementing data governance/quality policies and procedures in a regulated environment
- Technology Skills: Familiarity with data management technologies and tools, such as data catalogues, data lineage, and data quality tools
- Stakeholder Management: Proven history of working with both technical and non-technical stakeholders, bridging the gap between data teams and business units
Data Quality Analyst employer: Hanson Lee
Contact Detail:
Hanson Lee Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Quality Analyst
✨Tip Number 1
Familiarise yourself with the specific data governance frameworks and policies used in the insurance sector. Understanding the regulatory requirements and best practices will not only enhance your knowledge but also demonstrate your commitment to the role.
✨Tip Number 2
Network with professionals already working in data quality or governance within the insurance industry. Attend relevant events or join online forums to gain insights and potentially get referrals that could help you stand out in the application process.
✨Tip Number 3
Showcase your experience with data management technologies by discussing specific tools you've used in previous roles. Being able to speak confidently about your familiarity with data catalogues and quality tools can set you apart from other candidates.
✨Tip Number 4
Prepare to discuss how you've successfully collaborated with both technical and non-technical stakeholders in the past. Highlighting your ability to bridge gaps between teams will demonstrate your value as a Data Quality Analyst in our organisation.
We think you need these skills to ace Data Quality Analyst
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in data governance and management. Emphasise any specific projects or roles where you've developed and implemented data quality policies, especially in a regulated environment.
Craft a Compelling Cover Letter: In your cover letter, express your motivation for applying to this role. Mention your familiarity with data management technologies and how your experience aligns with the responsibilities outlined in the job description.
Highlight Stakeholder Management Skills: Since the role requires collaboration with various stakeholders, provide examples of how you've successfully worked with both technical and non-technical teams in the past. This will demonstrate your ability to bridge gaps and enhance data quality.
Showcase Continuous Learning: Mention any relevant training or certifications you have completed related to data governance or quality. This shows your commitment to staying updated in the field and your proactive approach to professional development.
How to prepare for a job interview at Hanson Lee
✨Showcase Your Data Governance Experience
Be prepared to discuss your previous roles in data governance and management. Highlight specific projects where you developed or implemented data quality policies, as this will demonstrate your expertise and relevance to the role.
✨Understand the Insurance Sector
Since experience in the insurance sector is crucial for this position, make sure you research the industry thoroughly. Familiarise yourself with common data challenges faced in insurance and be ready to discuss how your skills can address these issues.
✨Prepare for Stakeholder Management Questions
Expect questions about your experience working with both technical and non-technical stakeholders. Prepare examples that illustrate how you've successfully bridged gaps between data teams and business units, showcasing your communication and collaboration skills.
✨Demonstrate Your Passion for Data Quality
Express your enthusiasm for data quality and governance during the interview. Share any initiatives you've led or participated in that advocate for data quality best practices, as this will show your commitment to enhancing data-driven decision-making.
