Senior Data Specialist

• Define and implement the enterprise data architecture vision across on-prem and cloud platforms, evolving beyond traditional Oracle-based setups.
• Transform current data teams by promoting modernization, automation, and reduced custom coding in favor of scalable, industrialized solutions.
• Guide the transition toward externalized development (nearshoring/offshoring) and oversee delivery quality, alignment, and compliance.

AI & Data Intelligence Enablement

• Design and drive AI-enablement frameworks, including semantic search, LLM integration, RAG pipelines, knowledge assistants, and document intelligence.
• Introduce and pilot emerging AI technologies (vector stores, graph databases, embeddings, retrieval frameworks) with hands-on prototype validation.

Collaboration & Implementation

• Serve as the main point of contact for data initiatives (Data Hub, Self-Service Analytics, RAG-based applications)
• Coordinate with nearshore external development teams, validate deliverables, and oversee adherence to architectural standards.
• Communicate effectively across business, technical, and leadership stakeholders to translate concepts into actionable value.

Governance & Compliance

• Define and enforce governance frameworks including glossary, cataloging, and metadata management
• Ensure consistency across data assets regardless of platform: Oracle, Azure, AWS, Power BI, or other cloud ecosystems.
• Act as key liaison with security, legal, and compliance teams to align with evolving data and AI regulations.

Continuous Improvement

• Stay informed on emerging technologies in data and AI
• Lead initiatives to improve data quality, accessibility, and readiness for AI workloads

Do you have experience in Supervising experience?, Do you have a Master's degree?, * Strong background in data architecture or data engineering, with proven experience in data modernization and exposure to AI-focused systems

• Hands-on experience with cloud-based data platforms (AWS, Oracle Cloud Infrastructure)
• Very good understanding of data warehouse concepts, data governance, metadata management, and stewardship practices.
• Exposure to AI technologies: LLM integration, embeddings, RAG, semantic search, knowledge graph, vector databases (Neo4j, Pinecone, FAISS, etc.)
• Hands-on experience in at least one AI-related pilot or proof of concept (not just theoretical).
• Experience managing or supervising external development partners (nearshore/offshore), including quality, delivery, and alignment.
• Strong communication, mentoring ability, and capability to drive change within technical teams.
• Strategic mindset, curiosity, and strong desire to innovate and build scalable data and AI capabilities for the future.