As a Sr. Staff Data Architect, you will occupy one of the most senior technical leadership positions within our Enterprise Data AI Organization. You will treat data as the ultimate enterprise asset, establishing the foundational architecture, governance, and strategy for our entireprise data lakehouse.

Your primary mandate is to design and govern the AI Context Layer for the entire enterprise. You will oversee the complete lifecycle of 45+ data products across our Studio, Finance, and Enterprise domains, transforming a massive multi-domain data ecosystem into an interconnected, machine-readable semantic engine. This is a highly influential role requiring a unique blend of visionary enterprise architecture, deep mathematical/logical modeling, and a "builder" mindset capable of writing code and shipping rapid proofs-of-concept to validate cutting-edge AI data frameworks.

Role Responsibilities:

Enterprise Ontology Semantic Governance

- Multi-Domain Canonical Modeling: Lead the rationalization and creation of cross-domain canonical models (e.g., content hierarchies, unified customer identity spines, financial ledgers) that seamlessly unify data across deeply siloed source applications.
- Universal Semantic Layer Governance: Set the technical standard and reference architecture for a universal semantic layer across 45 distinct data products, ensuring unified definitions of complex metrics and business entities across Snowflake, Databricks, or Microsoft Fabric.
- Architect the Enterprise AI Context Layer: Define, scale, and govern the global Enterprise Ontology (abstract domain maps, business entity classes, and entity-relationship rules) that serves as the backbone for Agentic AI, LLM grounding, and GraphRAG systems.

Rapid Prototyping AI-Assisted Engineering

- AI-Native Prototyping: Leverage up-to-date AI-native development environments (such as Cursor, GitHub Copilot) to rapidly generate, iterate, and document blueprints for complex enterprise data schemas and metadata layers.
- Hands-on Proof of Concepts (PoCs): Actively build, code, and deploy functional,



lightweight PoCs to stress-test and validate semantic architectures, semantic views, and AI context-retrieval mechanisms before handing them off to core engineering squads.

Well-Architected Frameworks Platform Scalability

- Establish the Well-Architected Data Framework: Define and enforce enterprise pillars for data architecture focusing heavily on scalability, absolute data integrity, fault-tolerant schema evolution, and FinOps-driven cost optimization.
- High-Performance Execution: Ensure that semantic abstractions do not compromise physical database execution, optimizing massive, denormalized lakehouse engines for sub-second analytical and real-time AI retrieval performance.
- Automated Data Contracts: Implement and mandate automated data quality frameworks, CI/CD schema pipelines, and data contracts across domain boundaries to protect downstream AI agents from upstream changes.

Collaboration Communication:

- Help us stay ahead of the curve by working closely with data engineers, stream processing specialists, API developers, our DevOps team, and analysts to design systems which can scale elastically.
- Work closely with business analysts business users to understand data requirements.
- Provide technical leadership and mentorship to junior team members.
- Help build and maintain foundational data products such as but not limited to Finance, Titles, Content Sales, Theatrical, Consumer Products etc.
- Work with data platform product teams to bring the new features into live through PoC evaluations, feasibility checks, TCO rationalization.

Qualifications Experiences:

- 12+ years of deep data architecture and engineering experience, with a proven trajectory of operating in a Staff, Principal, or Enterprise Architect capacity.
- Scale and Scope:



Demonstrated success governing, designing, or managing large-scale data lakehouses containing dozens of concurrent data products or domains in production.
- Platform Mastery: Deep, expert-level architectural command of at least one major modern lakehouse ecosystem Snowflake (Dynamic Tables, Cortex, Horizon), Databricks (Unity Catalog, Delta Live Tables), or Microsoft Fabric alongside underlying cloud infrastructure (AWS/GCP/Azure).
- Advanced Modeling Expertise: Absolute mastery over diverse data modeling languages and paradigms including Relational, Dimensional (Kimball), Data Vault 2.0, and Graph/Ontological modeling frameworks.
- Hands-On AI-Augmented: Must be highly proficient in Python, SQL, and modern AI development acceleration tools (Cursor, LLM-prompted code generation) with a strong desire and ability to write code and spin up rapid, validating PoCs.
- The "Enterprise Data Translator": Outstanding executive presence and communication skills, with a track record of translating highly abstract semantic concepts (ontologies, knowledge graphs) into compelling business value narratives for C-suite and business stakeholders.
- Natural ability to navigate high-ambiguity environments, steer multi-million dollar architecture roadmaps, and align disparate technical engineering teams around a singular enterprise vision.

Preferred/Nice-to-Have Experience:

- Direct experience architecting semantic graphs or metadata abstraction layers specifically designed to optimize multi-agent AI workflows and enterprise RAG patterns.
- Familiarity with enterprise metric storage paradigms and semantic middleware tools (e.g., dbt Semantic Layer, Cube).
- Experience operating within the Media Entertainment Industry (Studio operations, content sales distribution networks, consumer digital platforms).

Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Sr. Staff, Data Architect (Hyderabad)
🏢 Warner Bros. Discovery
📍 Hyderabad