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- Tasks: Design and validate data models for enterprise initiatives in the London Market insurance sector.
- Company: Join a leading firm in the London insurance market, driving data innovation.
- Benefits: Enjoy hybrid work options and competitive daily rates up to £644.
- Why this job: Be part of a dynamic team shaping data strategies with real-world impact.
- Qualifications: Experience in data modeling and London Market insurance is essential.
- Other info: Contract runs until 31/12/2025; only successful applicants will be contacted.
The predicted salary is between 50000 - 90000 £ per year.
We are seeking an experienced Data Modeller with proven expertise in the London Market insurance sector. The successful candidate will play a key role in designing and validating data models that support enterprise data initiatives. This includes working closely with data engineers, architects, and business stakeholders to ensure data structures are scalable, accurate, and aligned with business needs.
Key Skills/Requirements:
- Design and maintain conceptual, logical, and physical data models to support reporting, analytics, and operational systems.
- Collaborate with data engineers and analysts to ensure models are implemented correctly and efficiently.
- Translate complex business requirements into scalable and maintainable data structures.
- Ensure data models comply with data governance, compliance, and London Market regulatory standards.
- Document data definitions, relationships, and lineage using industry-standard modeling tools.
- Support data quality initiatives by identifying gaps and inconsistencies in source systems and downstream usage.
Qualifications:
- London Market insurance experience is essential, including familiarity with market data structures and regulatory reporting.
- Strong experience in data modeling (conceptual, logical, physical) using tools such as Erwin, ER/Studio, or dbt.
- Solid understanding of data warehousing, data lakes, and enterprise data architecture.
- Proficiency in SQL and experience working with cloud data platforms (e.g., Azure, AWS, GCP).
- Familiarity with data governance frameworks, metadata management, and data cataloging tools.
- Excellent communication and documentation skills, with the ability to explain complex data concepts to non-technical stakeholders.
Preferred Skills:
- Experience with insurance platforms such as Guidewire, Duck Creek, or legacy PAS systems.
- Knowledge of Delta Lake, Apache Spark, and data pipeline orchestration tools.
- Exposure to Agile delivery methodologies and tools like JIRA, Confluence, or Azure DevOps.
- Understanding of regulatory data requirements such as Solvency II, Core Data Record (CDR), or Blueprint Two.
All profiles will be reviewed against the required skills and experience. Due to the high number of applications we will only be able to respond to successful applicants in the first instance. We thank you for your interest and the time taken to apply!
Contact Detail:
Placed Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Modeler (London Area)
✨Tip Number 1
Network with professionals in the London Market insurance sector. Attend industry events or join relevant online forums to connect with potential colleagues and learn about the latest trends and requirements in data modelling.
✨Tip Number 2
Familiarise yourself with the specific data modelling tools mentioned in the job description, such as Erwin or dbt. Consider taking short courses or tutorials to enhance your skills and demonstrate your commitment to mastering these tools.
✨Tip Number 3
Prepare to discuss your experience with data governance and compliance, especially in relation to the London Market. Be ready to provide examples of how you've ensured data quality and adhered to regulatory standards in previous roles.
✨Tip Number 4
Showcase your ability to communicate complex data concepts clearly. Practice explaining your past projects to non-technical stakeholders, as this skill is crucial for collaborating with business teams and ensuring alignment with their needs.
We think you need these skills to ace Data Modeler (London Area)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in the London Market insurance sector. Emphasise your data modelling skills and any relevant tools you've used, such as Erwin or dbt.
Craft a Strong Cover Letter: Write a cover letter that specifically addresses the key skills mentioned in the job description. Explain how your background aligns with the requirements, particularly your experience with data governance and regulatory standards.
Showcase Relevant Projects: Include examples of past projects where you designed and maintained data models. Detail your role in collaborating with data engineers and stakeholders to demonstrate your teamwork and communication skills.
Proofread Your Application: Before submitting, carefully proofread your application for any errors or inconsistencies. A polished application reflects your attention to detail, which is crucial for a Data Modeler role.
How to prepare for a job interview at Placed
✨Showcase Your London Market Experience
Make sure to highlight your experience in the London Market insurance sector during the interview. Discuss specific projects or roles where you designed data models that comply with regulatory standards, as this is crucial for the role.
✨Demonstrate Technical Proficiency
Be prepared to discuss your experience with data modelling tools like Erwin or dbt. You might be asked to explain how you've used these tools to create conceptual, logical, and physical data models, so brush up on your technical knowledge.
✨Communicate Clearly with Non-Technical Stakeholders
Since the role involves explaining complex data concepts to non-technical stakeholders, practice articulating your thoughts clearly. Use simple language and relatable examples to demonstrate your ability to bridge the gap between technical and non-technical teams.
✨Prepare for Scenario-Based Questions
Expect scenario-based questions that assess your problem-solving skills. Think of examples where you identified gaps in data quality or compliance issues and how you addressed them. This will show your proactive approach and understanding of data governance.
