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?

• 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.

• 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. Big Query) 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 enterprise assets, suppliers, and business processes.
• Facilitate Architecture Review Board (ARB) processes for data and AI initiatives, ensuring alignment between project delivery and architectural intent.
• Align all data architecture decisions with regulatory and compliance requirements…