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For Companies At a Glance
- Tasks: Design and deliver enterprise-grade data models for warehouses, lakes, and vaults.
- Company: Join a leading organisation in London's financial markets.
- Benefits: Contract role with hands-on experience in a dynamic environment.
- Why this job: Perfect for passionate Data Modelers wanting to make an impact with big data.
- Qualifications: 5 years of data modelling experience and 2 years with Data Vault techniques required.
- Other info: Not suitable for architects or generalists; focus on dedicated data modelling.
The predicted salary is between 43200 - 72000 ÂŁ per year.
Contract Opportunity: Data Modeler – London Markets
We’re looking for a dedicated Data Modeler to join a leading organisation in the London markets. This is a hands-on contract role (not an Architect, not a Developer-who-dabbles, not a Manager-in-hiding). You’ll be laser-focused on designing and delivering enterprise-grade data models across warehouses, lakes, and vaults.
The Role
You’ll be responsible for:
- Designing and developing conceptual, logical, and physical data models .
- Implementing models across RDBMS, ODS, data marts, and data lakes (SQL/NoSQL).
- Translating business needs into robust, long-term data models.
- Applying Data Vault 2.0 techniques in real-world implementations.
- Supporting metadata management, governance, and best practices with tools like Erwin or ER/Studio .
What We’re Looking For (Must-Haves)
- 5+ years hands-on experience as a Data Modeler (your CV should scream “Data Modeler,” not hide it in bullet 17).
- 2+ years hands-on Data Vault modelling.
- Strong background in data warehouses, data lakes, and enterprise big data platforms in multi-data-centre contexts.
- Proficiency with metadata management and data modelling tools (Erwin, ER/Studio, or similar).
Nice-to-Haves
- ETL/Data Transformation experience – ideally Qlik Compose or AWS Glue (alternatives like DataStage, SSIS acceptable).
- Cloud exposure (AWS preferred).
- Familiarity with modern dev methodologies (Agile/Scrum, TDD).
- Knowledge of testing tools, scripting, and data cleansing techniques.
Who Will Not Be Considered
To save everyone time, please note we are not looking for:
- Architects (too high-level, not hands-on enough).
- Developers who only list “data modelling” as one of many tasks.
- Generalists who do “a bit of everything.”
- Candidates focused on ETL development, management, or project coordination.
This is a contract role in London’s financial markets , offering the chance to work with enterprise-scale data in a dedicated modelling capacity. If your CV shows a consistent trajectory as a Data Modeler , we’d love to see it.
Data Modeler employer: CBSbutler
Contact Detail:
CBSbutler Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Modeler
✨Tip Number 1
Make sure to highlight your hands-on experience as a Data Modeler in your conversations. When networking or during interviews, focus on specific projects where you designed and implemented data models, especially using Data Vault 2.0 techniques.
✨Tip Number 2
Familiarise yourself with the tools mentioned in the job description, like Erwin or ER/Studio. If you can, practice using these tools before your interview, so you can confidently discuss your proficiency and how you've used them in past roles.
✨Tip Number 3
Connect with professionals in the London markets through platforms like LinkedIn. Engaging with industry-specific groups can provide insights into current trends and may even lead to referrals for the Data Modeler position.
✨Tip Number 4
Prepare to discuss your understanding of multi-data-centre contexts and enterprise big data platforms. Be ready to share examples of how you've navigated challenges in these environments, as this will demonstrate your suitability for the role.
We think you need these skills to ace Data Modeler
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience specifically as a Data Modeler. Use clear headings and bullet points to showcase your 5 years of hands-on experience, particularly in Data Vault modelling and enterprise data platforms.
Showcase Relevant Skills: Emphasise your proficiency with metadata management and data modelling tools like Erwin or ER/Studio. Include any experience with ETL/Data Transformation tools such as Qlik Compose or AWS Glue, even if it's not your primary focus.
Craft a Strong Cover Letter: Write a cover letter that directly addresses the job description. Explain how your background aligns with the responsibilities of designing and delivering data models, and mention your familiarity with Agile methodologies and cloud exposure.
Highlight Specific Projects: If possible, include specific projects or achievements that demonstrate your ability to translate business needs into robust data models. This could be through case studies or examples of past work that align with the role's requirements.
How to prepare for a job interview at CBSbutler
✨Showcase Your Data Modelling Experience
Make sure your CV and interview responses highlight your hands-on experience as a Data Modeler. Be prepared to discuss specific projects where you designed and developed data models, especially focusing on your work with RDBMS, ODS, and data lakes.
✨Demonstrate Knowledge of Data Vault 2.0
Since the role requires applying Data Vault 2.0 techniques, brush up on these methodologies before the interview. Be ready to explain how you've implemented these techniques in past projects and the impact they had on the overall data architecture.
✨Familiarise Yourself with Metadata Management Tools
The job mentions tools like Erwin or ER/Studio for metadata management. If you have experience with these or similar tools, be sure to discuss it. If not, do some research to understand their functionalities and how they fit into data modelling best practices.
✨Prepare for Technical Questions
Expect technical questions that assess your understanding of data modelling concepts and practices. Review key topics such as conceptual, logical, and physical data models, and be ready to solve hypothetical scenarios or case studies related to data modelling.
