Data Management & AI

Duration:

6 months+
Location:

Barcelona or Madrid preferred (1 day/week on site)
Languages:

Spanish + English

Context
We are seeking a

hands-on Data Management & AI Lead

to define and implement a comprehensive framework for Unstructured Data Management in support of data and AI-driven initiatives.

This role will focus on establishing standards, rules, and frameworks that ensure data reliability, consistency, and reusability, enabling scalable data and AI solutions in close collaboration with internal stakeholders.

The position is highly operational and execution-oriented, with direct involvement in Data Management tools, Python-based processing, and AI/GenAI use cases.

Key Responsibilities
Develop the methodological foundation for

Unstructured Data Management

in line with industry best practices.
Define and document standards, rules, and frameworks to ensure data quality, consistency, and reusability.
Collaborate with internal stakeholders to gather feedback and align with strategic objectives.




Pilot innovative Unstructured Data Frameworks and integrate findings into operational practices.
Evaluate technology vendors, benchmark solutions, and recommend suitable tools and platforms.
Assess document processing techniques such as OCR, NLP, and VLM, as well as metadata management, ontologies, and vector database solutions.
Support transformation initiatives related to data exploitation for AI and analytical purposes.
Configure Data Management tools directly in a hands-on manner.
Build parsing, extraction, and enrichment pipelines in Python.
Define Data Quality rules for

unstructured data

leveraging AI.

Must Have
Extensive experience in data management disciplines such as

Data Governance ,

Metadata ,

Data Quality ,

Data Architecture , and

Data Modelling .
Proven track record of delivering operational data management activities in complex environments.
Strong knowledge of document processing technologies, including

OCR, NLP,

and

📌 Data Manager AI (Madrid)
🏢 Senovo IT
📍 Madrid