Sr. Data Architect

Skydance is expanding its Enterprise Data & Analytics Group by adding a Sr. Data Architect who will treat data as a strategic and intelligent asset. The role involves co-owning the design of the foundational AI Context Layer, spanning enterprise ontology, canonical domain modelling and universal semantic layers.

• Define the vision, requirements and roadmap for an enterprise ontology and semantic layer that acts as the central source of truth for Agentic AI, LLM prompting and GraphRAG systems.
• Design and deploy semantic abstraction layers using native platform capabilities such as Snowflake Semantic Views, Databricks Unity Catalog/Liquid Clustering, Fabric Semantic Models, or semantic middleware such as dbt/Cube, to expose canonical data via intelligent APIs.
• Bridge the gap between physical big data performance and semantic meaning so that AI agents can reason through complex, recursive enterprise relationships without hallucinating.
• Lead the design and consolidation of foundational data domains (for example Studio, Finance, Content Sales and Consumer Products) into unified, cleansed canonical models within the Silver/Gold layers, abstraction-matching data from highly heterogeneous source systems.
• Implement resilient lakehouse patterns, including stream processing, event-driven pipelines and schema-on-read ingestion, to feed the canonical layers seamlessly.
• Partner with ML/DevOps teams to architect and maintain MLOps and LLMOps processes that keep context data, feature stores and vector embeddings continuously updated and monitored in production.
• Develop and enforce enterprise-wide standards for metadata management, data contracts and semantic version control through automated frameworks and CI/CD integration.
• Ensure data and its underlying business logic are highly discoverable and machine-readable, enabling business analysts through self-service BI and AI agents to query data with identical semantic consistency.
• Implement reusable data quality and identity resolution frameworks to handle complex object hierarchies, such as mappings across households, profiles and accounts, across the enterprise lakehouse.

• Bachelor's degree in Computer Science, Information Systems, Data Analytics, Information Technology or a 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 or lakehouses.
• 5+ years of experience with modern cloud data platforms, with deep architectural knowledge in at least one of Snowflake (Dynamic Tables, Cortex, Horizon), Databricks (Unity Catalog, Delta Live Tables) or Microsoft Fabric (OneLake, Semantic Models), alongside cloud ecosystems such as AWS or Azure.
• Advanced skill in data modelling across multiple paradigms: relational, dimensional (Kimball) and highly relational or flexible frameworks such as Data Vault 2.0 or graph-native concepts, along with proficiency in enterprise modelling tools.
• Demonstrated ability to build, maintain and version-control an ontology or semantic map, for example translating messy source attributes into a single, unified business entity layer.
• Solid understanding of FinOps, including managing and optimising cloud compute and storage consumption to maintain platform ROI while running complex semantic or AI workloads.