Senior Principal Enterprise Data Architect, AI Data Transformation

At GE Appliances, a Haier company, we come together to make “good things, for life.”  As the fastest-growing appliance company in the U.S., we’re powered by creators, thinkers and makers who believe that anything is possible and that there’s always a better way.  We believe in the power of our people and in giving them the freedom to explore, discover and build good things, together.  

The GE Appliances philosophy, backed by three simple commitments defines the way we work, invent, create, do business, and serve our communities: we come together, we always look for a better way, and we create possibilities. 

Interested in joining us on our journey? 

The Senior Principal Enterprise Data Architect – AI Data Transformation will serve as a strategic partner and governance leader within the Enterprise Architecture (EA) team of our global enterprise. This role combines advanced enterprise data architecture discipline with deep expertise in Artificial Intelligence infrastructure and Data Science enablement to plan, design, deploy, and execute technology solutions aligned to the organization's strategic roadmap.

The incumbent will be instrumental in operationalizing complex initiatives by significantly enhancing, evolving, and optimizing the enterprise data layer to make every data asset—across our global operations, supply chain, customer touchpoints, and connected products—AI-ready, AI-consumable, and AI-trustworthy. This role will champion EA and AI data governance frameworks, drive Hoshin goal attainment, and serve as a key liaison between IT, business operations, product engineering, data science teams, and the EA team to ensure technology investments are aligned to enterprise standards and strategic AI objectives.

Position

Senior Principal Enterprise Data Architect, AI Data Transformation

Location

USA, Louisville, KY

How You'll Create Possibilities

AI Data Layer Enhancement & Transformation (40%)

• Lead the architectural enhancement and evolution of the enterprise data layer, applying AI-first design principles to unify data across the enterprise value chain (R&D, supply chain, operations, and customer experience).

• Define, publish, and maintain the Enterprise AI Data Architecture Blueprint—the authoritative reference governing how data flows from source systems (e.g. ERP, CRM, PLM, IoT platforms) through transformation layers to AI models and business outcomes.

• Design and operationalize an Enterprise AI Data Readiness Framework that continuously assesses, scores, and improves data assets across five core dimensions: Completeness, Consistency, Timeliness, Representativeness, and Fairness.

• Architect and deploy enterprise-grade vector database infrastructure and build enterprise embedding pipelines that transform structured records, enterprise documents, product manuals, and operational logs into high-quality vector representations.

• Define the complete data architecture for Large Language Model (LLM) integration, including Retrieval-Augmented Generation (RAG) architecture to support enterprise copilots, customer service, and operational workflows.

• Design ultra-low latency data serving architectures and event-driven AI data pipelines that feed live AI models in production (e.g., real-time operational analytics, predictive maintenance, and customer insights).

• Establish an enterprise Synthetic Data Generation capability to augment scarce datasets, generate privacy-safe alternatives to sensitive data, and simulate operational edge cases.

Enterprise Architecture Strategy & Governance (35%)

• Serve as a strategic partner and governance leader within the EA team, applying and evolving enterprise architecture frameworks (TOGAF, Zachman) with AI-era extensions tailored for a large-scale, complex enterprise environment.

• Architect modern cloud data warehouse and Lakehouse solutions (e.g. BigQuery) as the unified, ACID-compliant foundation for both analytical and AI/ML workloads on a single governed storage layer.

• Define and enforce data contracts between data producers (e.g., business operations, product engineering) and AI consumers across all domains to ensure schema, quality, freshness, and semantic consistency.

• Lead Master Data Management (MDM) strategy with AI entity resolution, enrichment, and disambiguation capabilities embedded in the MDM layer (covering Product, Material, Supplier, and Customer domains).

• Govern metadata management, data cataloging, and data lineage (e.g. Collibra) and design semantic/context data layers/Knowledge Graph infrastructure to map complex relationships between enterpris