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Tier-1 brand, popular data role, metro context, and broad technical requirements increase competition.
Deep data-platform, knowledge graph, and LLM-integration expertise limits cross-industry transferability.
Many mandatory technical skills and specialized platform experience indicate high shortlisting rigidity.
Lead hands-on design and implementation of AI-ready data platforms including semantic data layers, knowledge graphs, and metadata-driven architectures focusing on Snowflake, SQL, and Python.
Define and evolve business semantic models, ontologies, and governed data products to provide consistent data access for analytics, applications, and AI workloads.
Engage with business stakeholders to translate strategic objectives into actionable data engineering initiatives and oversee technical delivery and architectural coherence.
Proven senior technical experience with Snowflake, SQL, Python, semantic data models, metadata-driven architectures, and Graph Database technologies like Neo4j.
Experience building AI-ready data platforms incorporating vector databases, document retrieval, and RAG architectures along with LLM and AI agent integration.
Strong knowledge of enterprise metadata management, data governance, data pipeline design, and cloud platforms (Azure or AWS preferred).
Work Experience Required: Proven senior technical leadership experience; explicit years not mentioned.
Experienced senior individual contributor and technical leader comfortable balancing hands-on engineering with stakeholder engagement and architectural decisions.
Strong background in complex data architecture, AI integration, and scalable data platform development in fast-paced, dynamic environments.
Technical authority with ability to influence prioritization and collaborate across business and technical teams to achieve strategic alignment.