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Global brand and metro location increase competition, but senior, niche role limits applicant pool.
Highly specialized lakehouse, semantic modeling and ontology skills limit cross-industry transferability.
Explicit 12+ years, mandatory deep lakehouse/platform mastery and modeling plus hands-on coding make filters highly strict.
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Design and govern the AI Context Layer and enterprise ontology for 45+ data products across multiple domains (Studio, Finance, Enterprise).
Lead cross-domain canonical modeling to unify data across siloed source systems and establish a universal semantic layer architecture.
Develop and deploy rapid AI-augmented prototypes and proof-of-concepts to validate semantic architectures and AI data frameworks before handoff to engineering teams.
12+ years of data architecture and engineering experience, with prior roles at Staff, Principal, or Enterprise Architect level.
Expertise in at least one major cloud data lakehouse platform: Snowflake, Databricks, or Microsoft Fabric.
Advanced data modeling skills in relational, dimensional (Kimball), Data Vault 2.0, and graph/ontological paradigms.
Proficiency in Python, SQL, and AI-assisted development tools (e.g., Cursor, GitHub Copilot).
Experienced enterprise architect comfortable steering multi-domain, multi-product data lakehouse ecosystems at scale.
Skilled at translating complex semantic concepts into business value for executive-level stakeholders and aligning cross-functional teams.
Strong hands-on builder with ability to rapidly prototype AI-context data products and scalable, well-architected data frameworks under ambiguity.