Sr. Data Architect

Description

The company is a global media and entertainment business with three segments: Studios, Direct-to-Consumer, and TV Media.

The team builds the next generation of that business around creativity and technology, and works fast, openly and with a shared goal.

The enterprise data and analytics group is hiring a Senior Data Architect to shape how data supports company-wide decisions and agentic AI systems.

Responsibilities

• Co-own the design of the core AI context layer, covering enterprise ontology, canonical domain modeling and universal semantic layers.
• Set the vision, requirements and roadmap for an enterprise ontology and semantic layer that serves as a single source of truth for agentic AI, LLM prompting and GraphRAG systems.
• Design and deploy semantic abstraction layers using native platform features such as Snowflake Semantic Views, Databricks Unity Catalog with Liquid Clustering, Fabric Semantic Models, or dbt and Cube, and expose canonical data through intelligent APIs.
• Connect physical big data performance with business meaning so AI agents can reason over complex, recursive relationships without hallucinating.
• Lead the design and consolidation of core domains such as Studio, Finance, Content Sales and Consumer Products into unified, cleansed canonical models in the Silver and Gold layers, mapping heterogeneous source systems to common abstractions.
• Implement lakehouse patterns, including stream processing, event-driven pipelines and schema-on-read ingestion, to keep canonical layers fed reliably.
• Work with ML and DevOps teams to design and run MLOps and LLMOps processes that keep context data, feature stores and vector embeddings current and monitored in production.
• Create and enforce company-wide standards for metadata, data contracts and semantic version control, automated through CI/CD.
• Make data and its business logic discoverable and machine-readable, so business analysts using self-service BI and AI agents get the same semantic results.
• Build reusable data quality and identity resolution frameworks for hierarchies such as households, profiles and accounts across the lakehouse.
• Partner with senior leadership, data engineering and AI/ML teams to turn an optimized data lakehouse into an interconnected knowledge engine that reasons on its own.

Requirements

• A bachelor's degree in computer science, information systems, data analytics, information technology or a similar field.
• 5+ years of data engineering and data architecture experience, including a dedicated enterprise or domain architect role.
• A proven record of designing, scaling and implementing canonical models and semantic layers on very large enterprise data lakes or lakehouses.
• 5+ years with modern cloud data platforms, with deep architectural knowledge of at least one of Snowflake (Dynamic Tables, Cortex, Horizon), Databricks (Unity Catalog, Delta Live Tables) or Microsoft Fabric (OneLake, Semantic Models), and experience with AWS or Azure.
• Advanced data modeling across relational, Kimball dimensional, and Data Vault 2.0 or graph-native approaches, plus skill with enterprise modeling tools.
• Experience building, maintaining and version-controlling an ontology or semantic map that turns messy source attributes into one unified business entity layer.
• A solid understanding of FinOps, including managing and optimizing cloud compute and storage spend to protect platform ROI while running complex semantic and AI workloads.

Conditions

Qualified candidates with a disability who need adjustments or accommodations during the application or recruitment process can request them through the company's accessibility page.