Your Role:

We are expanding our Enterprise Data & Analytics Group by adding a Sr. Data Architect. The ideal candidate will treat data as a strategic and intelligent asset, architecting the framework that empowers our entire organization to make high-stakes, data-driven decisions and powers our next-generation Agentic AI ecosystems. You will co-own the design of the foundational AI Context Layer—from enterprise ontology and sophisticated canonical domain modeling to the building of universal semantic layers. This is a high-visibility role where you will spearhead modernization efforts, partnering with senior leadership, data engineering, and AI/ML teams to transform an optimized data lakehouse into an interconnected, self-reasoning knowledge engine.

Role Accountabilities:

Semantic Architecture & AI Context Layer

• Architect the AI Context Layer: Define the vision, requirements, and roadmap for an Enterprise Ontology and Semantic Layer that acts as the "world map" and central source of truth for Agentic AI, LLM prompting, and GraphRAG systems.
• Build the Semantic Layer: Design and deploy semantic abstraction layers (using native platform capabilities like Snowflake Semantic Views, Databricks Unity Catalog/Liquid Clustering, Fabric Semantic Models, or semantic middleware like dbt/Cube) to expose canonical data via intelligent APIs.
• Drive Data Innovation: Bridge the gap between physical big data performance and semantic meaning, ensuring that AI agents can reason through complex, recursive enterprise relationships without hallucinating.

Technical Design & Canonical Engineering

• Build Canonical Data Products: Lead the design and consolidation of foundational data domains (e.g., Studio, Finance, Content Sales, Consumer Products) into unified, cleansed Canonical Models within the Silver/Gold layers, successfully abstraction-matching data from highly heterogeneous source systems.
• Modernize the Medallion Stack: Implement cutting-edge, resilient lakehouse patterns—including stream processing, event-driven pipelines, and schema-on-read ingestion—to feed the canonical layers seamlessly.
• Operationalize AI Context Pipelines: Partner with ML/DevOps teams to architect and maintain MLOps and LLMOps processes that ensure context data, feature stores, and vector embeddings are continuously updated and monitored in production.

Governance, Quality & Semantic Integrity

• Standardize & Automate Contracts: Develop and enforce enterprise-wide standards for metadata management, data contracts, and semantic version control through automated frameworks and CI/CD integration.
• Enable AI & Data Democracy: Ensure data and its underlying business logic are highly discoverable and machine-readable, allowing both traditional business analysts (via self-service BI) and AI Agents to query data with identical semantic consistency.
• Ensure Relationship Integrity: Implement reusable data quality and identity resolution frameworks to handle complex object hierarchies (e.g., mappings across households, profiles, and accounts) across the enterprise lakehouse.

Qualifications & Experiences:

• Bachelor’s degree in Computer Science, Information Systems, Data Analytics, Information Technology or similar major
• 5+ years of experience in data engineering and data architecture in a dedicated Enterprise or Domain Architect capacity.
• Proven track record of designing, scaling, and implementing Canonical Models and Semantic Layers that sit on top of massive Enterprise Data Lakes/Lakehouses.
• 5+ years of experience with modern cloud data platforms, specifically possessing deep architectural knowledge in at least one of the following: Snowflake (Dynamic Tables, Cortex, Horizon), Databricks (Unity Catalog, Delta Live Tables), or Microsoft Fabric (OneLake, Semantic Models), alongside cloud ecosystems like AWS or Azure.
• Advanced skill in Data Modeling across multiple paradigms: Relational, Dimensional (Kimball), and highly relational/flexible frameworks like Data Vault 2.0 or Graph-native concepts, along with proficiency in enterprise modeling tools.
• Demonstrated ability to build, maintain, and version-control an ontology or semantic map (e.g., translating messy source attributes into a single, unified business entity layer).
• Solid understanding of FinOps—managing and optimizing cloud compute/storage consumption to maintain platform ROI while running complex semantic or AI workloads.