Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Shape and govern data quality in a dynamic insurance environment.
- Company: Join a growing Data Office within a leading insurance firm.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Why this job: Make a real impact on data quality and drive meaningful outcomes.
- Qualifications: 5-8 years in data quality or governance, with strong insurance experience.
- Other info: Collaborative team culture focused on innovation and continuous improvement.
The predicted salary is between 48000 - 72000 £ per year.
Overview
The Senior Data Quality Analyst plays a critical role in shaping and governing data quality across a complex, data-driven insurance environment. Sitting within a growing Data Office, this role acts as the primary decision and interpretation layer for data quality controls—ensuring validations are meaningful, prioritised, and drive the right outcomes. The role focuses on reducing noise, improving control effectiveness, leading root cause analysis, and laying the foundations for data quality scoring, anomaly detection, and prevention, while working closely with data owners, product, and technology teams to ensure data quality aligns with business appetite and delivers real value.
Must-Have Experience
- 5-8+ years' experience in data quality, data governance, analytics, risk/control or data operations roles.
- Strong insurance domain experience - specialty insurance strongly preferred, P&C insurance as a minimum - with hands-on exposure to underwriting, exposure or risk data.
- Experience working with large, complex datasets where validation volume is high.
- Strong understanding of insurance concepts.
- Proven experience interpreting data quality issues at scale and making judgement calls on rule vs education vs process fix and signal vs noise.
- Demonstrated ability to lead Root Cause Analysis (RCA) and challenge upstream processes constructively.
- Strong analytical mindset, comfortable reasoning about data behaviour and trends, distributions, outliers, and anomalies and aggregate and cross-dataset consistency.
- Working knowledge of SQL and/or Python sufficient to query datasets independently, explore data distributions and patterns, validate assumptions behind proposed controls, articulate validation logic and thresholds clearly.
- Ability to work effectively before tooling is fully mature, shaping how DQ tools, scoring, and anomaly detection should be used rather than waiting for perfect solutions.
Data Quality Analyst - OutsideIR35 - Insurance in City of London employer: Oliver James
Contact Detail:
Oliver James Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Quality Analyst - OutsideIR35 - Insurance in City of London
✨Tip Number 1
Network like a pro! Reach out to your connections in the insurance and data quality space. Attend industry events or webinars, and don’t be shy about introducing yourself. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data quality projects, especially those involving root cause analysis and complex datasets. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on your SQL and Python skills. Be ready to discuss how you've tackled data quality issues in the past and how you approach anomaly detection. Practice makes perfect!
✨Tip Number 4
Don’t forget to apply through our website! We’ve got some fantastic opportunities waiting for you, and applying directly shows your enthusiasm. Plus, it’s a great way to get noticed by our hiring team!
We think you need these skills to ace Data Quality Analyst - OutsideIR35 - Insurance in City of London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV speaks directly to the role of Data Quality Analyst. Highlight your experience in data quality, governance, and analytics, especially in the insurance sector. 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 your background makes you a perfect fit for our team. Don't forget to mention any specific projects or achievements that showcase your expertise.
Showcase Your Technical Skills: Since this role requires knowledge of SQL and Python, make sure to include any relevant projects or experiences where you've used these tools. We love seeing how you've tackled complex datasets and improved data quality in your previous roles!
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It helps us keep track of your application and ensures you don’t miss out on any important updates. Plus, it’s super easy!
How to prepare for a job interview at Oliver James
✨Know Your Data Inside Out
Make sure you brush up on your knowledge of data quality, governance, and analytics. Be prepared to discuss specific examples from your past experience, especially in the insurance domain. Highlight how you've tackled data quality issues and what outcomes you achieved.
✨Showcase Your Analytical Skills
During the interview, demonstrate your analytical mindset by discussing how you approach data behaviour, trends, and anomalies. Use real-life scenarios where you've successfully led Root Cause Analysis (RCA) to show your problem-solving skills and ability to challenge processes constructively.
✨Familiarise Yourself with SQL and Python
Since the role requires working knowledge of SQL and/or Python, be ready to talk about your experience with these tools. You might even want to prepare a few examples of how you've used them to query datasets or validate assumptions in your previous roles.
✨Understand the Business Context
It's crucial to align data quality with business needs. Research the company’s insurance products and their data challenges. Be prepared to discuss how you can help shape data quality controls that deliver real value and improve decision-making within the organisation.
