Data Manager Ai

Data Management & AI Duration: 6 months+Location: Barcelona or Madrid preferred (1 day/week on site)Languages: Spanish + EnglishContextWe 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 ResponsibilitiesDevelop 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 HaveExtensive 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 VLM.Deep understanding of unstructured data challenges, including volume, format heterogeneity, and lack of fixed schema.Strong operational and technical profile, rather than a purely theoretical one.Python proficiency, specifically for parsing, extraction, and enrichment pipelines.Ability to configure Data Management tools directly.Comfort using AI/GenAI, including LLMs, RAG, and auto-classification.Solid understanding of AI fundamentals, including embeddings, vector stores, and agents.Strong knowledge of the semantic layer, including metadata management, ontologies, and knowledge graphs.Nice to HaveFamiliarity with vendor evaluation, benchmarking, and solution assessment methodologies.DAMA certification.Hiring manager Contact: **

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 HaveExtensive 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 VLM.Deep understanding of unstructured data challenges, including volume, format heterogeneity, and lack of fixed schema.Strong operational and technical profile, rather than a purely theoretical one.Python proficiency, specifically for parsing, extraction, and enrichment pipelines.Ability to configure Data Management tools directly.Comfort using AI/GenAI, including LLMs, RAG, and auto-classification.Solid understanding of AI fundamentals, including embeddings, vector stores, and agents.Strong knowledge of the semantic layer, including metadata management, ontologies, and knowledge graphs.Nice to HaveFamiliarity with vendor evaluation, benchmarking, and solution assessment methodologies.DAMA certification.Hiring manager Contact: **