Data Modeller

magine is looking for a Data Modeller for building an audit-ready data warehouse for Finance & Risk to one of our banking clients.

Start date: 2026-11-23

End date: 2027-04-30

Location: Stockholm (3 days onsite, 2 days remote

Description

About the role

You will play a key role in transforming clients Finance & Risk Data Platform into a modern, scalable, and audit-ready enterprise data warehouse. This is a hands-on data modeler role with a particular focus on enterprise data modelling. As part of the FR360 Data Platform team, you will help establish a data architecture that enables reliable integration at enterprise scale while meeting the high standards of a regulated banking environment.

Your primary focus will be designing and implementing robust data models that support business reporting, regulatory requirements, historical data management, and data governance. You will work closely with data engineers, data architects, business stakeholders, and Finance & Risk specialists to ensure that data is accurate, traceable, and fit for purposes.

Key responsibilities include

• Take accountability for implementing and maintaining physical data models in the target data platform.
• Use SQL and other analytical techniques to understand source data structures, content, relationships, keys, grain, and data quality.
• Design target data models using scalable and maintainable patterns based on Data Vault 2.0 principles and methodologies.
• Define and promote standards for historical data management, auditability, lineage, metadata management, and data quality.
• Build and optimize ELT processes using Google Cloud Platform technologies, including BigQuery, Cloud Composer, dbt, and Dataform.
• Drive best practices in data modeling and warehouse design across the team.

You will also share responsibility for the team’s day-to-day data engineering work while taking a leading role in data modelling and data governance.

To thrive in this role, we believe you have

• Deep expertise in data modelling and enterprise data warehouse design.
• Proven hands-on experience delivering data models, from requirements gathering and source analysis through conceptual and logical modelling to physical design and production implementation.
• A track record of working with scalable data platforms that integrate large numbers of heterogeneous source systems.
• A strong ability to balance business requirements, regulatory expectations, and long-term maintainability.
• Strong communication and collaboration skills, with the ability to translate complex requirements into practical data solutions covering data structures, semantics, keys, and data quality.
• Strong SQL and Python skills.

It is beneficial if you also have

• Experience in banking, finance, or another regulated industry.
• Hands-on experience with Data Vault 2.0 modelling principles and implementation.
• Hands-on experience building and maintaining ELT pipelines and data integration solutions.
• Familiarity with GCP services and tools such as BigQuery, Cloud Composer, Apache Airflow, Dataform, and dbt, or with comparable cloud data platforms.
• Knowledge of data governance, regulatory reporting, or frameworks such as BCBS 239.