GCP Data Modeler

Xebia is a global AI-first, digital transformation, and engineering partner. With over 25 years of experience and a team of 5,000 professionals across 16 countries, we help organizations design and build scalable products, platforms, and data-driven solutions.

We specialize in Artificial Intelligence, Data and Cloud, Intelligent Automation, and Digital Products, combining deep technical expertise with a strong focus on engineering excellence and a people-first culture.

In the CEE region, we’re a team of nearly 1,000 experts delivering modern applications, data platforms, and AI solutions for clients such as Millennium, ING, Play, Edenred, Arabian Drilling, FedEx, Leroy Merlin, Truecaller, Volotea, Schmitz Cargobull, and many, many more. We work with leading technologies including AWS, Azure, GCP, Databricks, and Snowflake, and combine strong engineering culture with a consulting mindset and a continuous focus on growth and knowledge sharing.

You will be:

analyzing the existing Data Quality monitoring solution, including its data structures, relationships, dependencies and integration points,

designing target-state analytical and enterprise data models supporting Data Quality monitoring in GCP,

defining data structures, modeling patterns and development standards within dbt Core,

contributing to the design of the target cloud-native monitoring architecture,

establishing consistent data modeling standards, naming conventions and reusable patterns,

defining integration principles between the Data Quality solution and other Data Platform components,

mapping legacy data structures and processes to the target architecture,

identifying modeling gaps, dependencies and risks affecting the migration,

collaborating with Business Analysts to ensure that the target models reflect business and Data Quality requirements,

working closely with Data Engineers during implementation and supporting them in translating the models into scalable data solutions,

collaborating with Test Engineers during model validation, reconciliation and testing,

reviewing implemented data structures to ensure their alignment with the agreed models and standards,

contributing to technical, architectural and maintenance documentation,

creating and maintaining data model documentation, mapping specifications, data dictionaries and modeling guidelines,

communicating design decisions and technical concepts clearly to both technical and non-technical stakeholders.

Your profile:

extensive senior-level experience in Data Modeling,

proven experience designing analytical or enterprise data models,

hands-on experience working with Google Cloud Platform,

practical experience with dbt Core, including defining model structures, dependencies and development standards,

strong knowledge of SQL-based data environments,

experience working with cloud-based data platforms and modern data architectures,

good understanding of Data Quality concepts, controls and monitoring solutions,

ability to analyze an existing data architecture and design a clear, scalable target-state model,

experience supporting data migration or transformation initiatives,

experience collaborating closely with Data Engineering teams,

ability to define and document data modeling standards, naming conventions and integration principles,

strong analytical and problem-solving skills,

excellent communication and stakeholder collaboration skills,

ability to create clear technical and maintenance documentation,

ability to work independently and take ownership of data modeling decisions,

fluent English,

practical experience using AI-powered assistants, such as ChatGPT, Claude or Copilot, to improve productivity and quality in analytical and documentation work.

Work from the European Union region and a work permit are required.

Nice to have:

experience working in financial services or banking environments,

knowledge of Data Platform integration patterns,

experience supporting migrations from legacy data solutions to cloud-native platforms,

experience designing data models for Data Quality, observability or monitoring solutions,

familiarity with data reconciliation and validation processes,

experience working in complex enterprise data environments.

Recruitment Process:

CV review – HR call – Interview – Client Interview – Decision