(Senior) Data Modelling Engineer

You will be joining our Data Engineering team, which builds and evolves the data platform powering analytics, business intelligence, product insights and operational decision-making across the organization. In this role, you will take ownership of our data modelling layer within a modern cloud data platform. You will design scalable, resilient and auditable data models, drive modelling standards and shape the evolution of our data architecture using Data Vault 2.0, dbt, BigQuery and Cloud Composer / Airflow.

What to expect:

• Own the design and architecture of scalable data models using Data Vault 2.0, including Hubs, Links and Satellites, while defining and enforcing modelling standards, conventions and best practices.

• Transform raw data into structured, integrated and high-quality datasets that create value across the organization, embedding data quality, lineage, auditability and governance into all models.

• Design scalable, modular dbt frameworks and optimize BigQuery pipelines and storage for performance, cost, scalability and maintainability.

• Architect and oversee orchestration workflows with Cloud Composer / Airflow, while driving testing strategies, CI/CD practices, code quality and technical design reviews.

• Partner with analytics, data engineering and business stakeholders to translate requirements into robust data models, mentor engineers and resolve performance or scalability challenges.

Do you have experience in Google Cloud Platform?, * Extensive experience in data modelling with demonstrable expertise in Data Vault 2.0, combined with strong hands-on experience using dbt in production environments.

• Advanced proficiency in cloud data warehouses, ideally BigQuery, including query optimization, partitioning, clustering and cost management.
• Expert-level SQL skills and experience with large-scale, complex datasets, plus a strong understanding of modern data architectures such as Bronze / Silver / Gold and lakehouse patterns.
• Experience designing scalable and maintainable data platforms on hyperscalers such as GCP, AWS or Azure, supported by strong software engineering practices including version control, testing and CI/CD.
• Strong communication and stakeholder management skills, with the ability to influence technical standards across teams and apply automation or AI-assisted practices in data engineering where useful.