Enterprise Data Architect

• Degree or Licentiate in Computer Engineering, Telecommunications Engineering, or another equivalent qualification.
• Postgraduate training (Master's) in data architecture, data engineering, data management, or related areas will be valued.
• Certifications in enterprise architecture (e.g., TOGAF), in cloud data platforms (Microsoft Azure, AWS, or Google Cloud), and in data management and governance (e.g., DAMA-DMBOK / CDMP) will be valued.
• Consolidated experience in data architecture and data platforms (approximately 8-10 years or more).
• Demonstrable track record leading end-to-end implementations of data architectures and platforms.
• Previous hands-on technical experience (data engineering, integration, BI/analytics): although the position is not for execution, it is required to maintain solid and up-to-date technical judgment.
• Broad mastery of the tool landscape for a complete data architecture, both in on-premise and cloud environments and in both proprietary and open-source solutions, covering: data integration (ETL/ELT), data warehouse / lakehouse, data lakes, streaming processing and event-oriented architectures, data catalog, metadata and lineage, master data management (MDM), data quality, and data foundations for advanced analytics and AI.
• Solid knowledge of integration and interoperability patterns between heterogeneous systems (APIs as stable contracts, event orientation).
• Sufficient understanding of data governance frameworks (quality, security, GDPR, and compliance)
• Spanish: high or native level.
• English: high level (minimum B2/C1), to work with technical documentation, international manufacturers, and providers.
• Leadership of technical teams and projects, with a clear orientation toward delivery.
• Strategic vision combined with execution capacity.
• Ability to communicate with technical and business profiles, translating needs into solutions.
• Management of providers and integrators.
• Analytical rigor, autonomy, and the ability to influence without direct hierarchical authority.

Universidad de Navarra is driving an evolution of its management model in which data architecture constitutes a fundamental pillar. To lead this transformation from a technological standpoint, IT Services intends to incorporate a new role that will design and realize the data architecture upon which the management, teaching, and research of the coming years will be based.

To lead, from a technological dimension, the design, construction, and evolution of the University's data architecture, so that the institutional data strategy is translated into a real platform: reliable, interoperable, governed, and prepared for artificial intelligence.

The position exists to provide the University with the technological foundations—interoperable data layer, integration, platforms, and reference patterns—that allow information to flow in a reusable and decoupled manner through a heterogeneous ecosystem of systems (SaaS, PaaS, and on-premise). This is the person responsible for ensuring that the University's data objectives have a solid and sustainable architectural base over time.

They carry out their work in coordination with the Data Office—responsible for data governance—to which they provide the translation of its policies into architecture, and with the rest of IT Services, to ensure the technological coherence of the whole.

This is not a data science or analytical development position, but rather a technological architecture function that must robustly enable reporting, advanced analytics, and artificial intelligence uses.

1. Define and safeguard the target data architecture of the University—interoperable layer, decoupling of consumption, integration patterns (APIs and events)—maintaining the overall vision and the roadmap for the evolution of the data platform, in line with the IT Services strategic plan.
2. Lead end-to-end data architecture and platform projects, from design to implementation, coordinating internal teams and external providers and integrators, and assuming responsibility for their execution.
3. Evaluate the tool market and select and evolve the data technological stack—in on-premise and cloud environments, in both proprietary and open-source solutions—defining standards, blueprints, and reference integration contracts.
4. Provide the technological dimension of data governance in coordination with the Data Office, translating its quality, security, metadata, catalog, lineage, and interoperability policies into the architecture.
5. Establish data engineering standards, patterns, and best practices (modeling, ingestion, storage, event-oriented integration, observability) and ensure their homogeneous adoption across the organization.
6. Guarantee that the data architecture complies with applicable security, continuity, and regulatory requirements (ENS, NIS2, ISO 27001, GDPR).
7. Act as a technical reference and advisor for the University's data and artificial intelligence initiatives, collaborating with solution and systems architecture and with academic, research, and management areas.
8. Anticipate technological evolution in data and AI and propose modernization and investment decisions for the platform.