Data Architect / Data Modeler

One of our clients is seeking a Data Architect / Data Modeler

Contract basis - due to start ASAP and will be 4 months long.

Geneva / Hybrid

The role is focused on the setup phase of the initiative, defining data models, structures, flows, and caching principles required for orchestration, rather than implementing them.

It ensures that data is semantically consistent, normalised, and AI-ready, and provides a clear architectural specification that guides subsequent build and execution phases.

Key Responsibilities

• Define a canonical data model covering i.e. offers, orders, services, AI interactions, and identity, ensuring alignment across APIs, the client's standards.
• Establish data normalization and mapping rules to harmonize specific schemas and industry standards into a single consistent representation
• Specify AI/MCP data structures and contracts, ensuring AI-generated requests can be translated into structured API calls and responses remain deterministic and consumable
• Define the caching architecture, including:

o Data to be stored (context, responses, aggregated offers, metadata)

o Lifecycle rules (TTL, refresh, reuse, consistency)

o Trade-offs between performance and data accuracy

o Alignment with canonical models and orchestration needs

• Design end-to-end data flows from AI interaction through orchestration to systems, including mappings, transformations, and aggregation principles
• Define data governance standards, including metadata, lineage (logical level), documentation, and compliance with enterprise data policies
• Ensure the overall design provides a single canonical representation of the client's content, scalable to future capabilities such as booking, payment, and additional services

Experience & Skills

• 5+ years of experience in data modelling (logical/conceptual)
• Strong understanding of API data structures (REST, JSON/XML payloads) and AI/MCP interaction models
• Familiarity with industry data standards (aviation, travel, or similar is a plus)
• Experience with modelling tools (e.g., ER tools, UML, data modelling platforms)
• Strong documentation and communication skills