Senior Principal Enterprise Data Architect, AI Data Transformation

GE Appliances, a Haier company•Louisville, KY

1d•Hybrid

About The Position

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.

Requirements

Bachelor's degree in Computer Science, Data Science, Information Systems, Mathematics, Engineering, or a related technical field required.
15+ years of progressive experience in data-related roles, with a minimum of 5 years in Enterprise Data Architecture at enterprise scale.
3+ years of experience designing and architecting AI/ML data infrastructure (feature stores, vector databases, model serving layers, semantic layers).
Proven track record of leading enterprise data transformation programs with measurable AI and ML outcomes delivered in production environments.
Excellent oral and written presentation Skills
Works independently with limited supervision and operates autonomously
Working knowledge of enterprise architecture frameworks (e.g., TOGAF, Zachman)

Nice To Haves

Master's degree in Computer Science, Data Science, Artificial Intelligence, or a related field strongly preferred.
Prior experience architecting data solutions involving complex supply chains, ERP (SAP/Oracle), PLM, or large-scale IoT/telemetry is preferred.
Experience working in both Agile and Waterfall delivery environments
Project Management Professional (PMP) certification preferred
TOGAF or other EA framework certification preferred

Responsibilities

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. 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 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 without compromising AI agility.
Apply statistical expertise to validate data representativeness, distributions, class balance, and sampling strategies for AI training datasets (e.g., ensuring datasets accurately represent real-world operational realities).
Serve as a trusted advisor and primary point of contact for business and IT stakeholders on AI data-governed initiatives.
Build and maintain effective working relationships at all levels of DT Staff, Extended DT Staff, and business leadership.
Proactively identify risks, issues, dependencies, and bottlenecks; implement mitigation strategies to keep teams moving forward.
Partner with functional/business teams, DT teams, and other team members to solve problems collaboratively and deliver project objectives.
Provide architectural oversight and define enterprise standards for AI/ML-optimized data pipelines, guiding data engineering delivery teams from raw ingestion through feature engineering.
Define the architecture and integration patterns for the Enterprise Feature Store as the central hub of reusable, versioned ML features.
Establish DataOps and pipeline governance frameworks, guiding delivery teams on best practices for CI/CD, automated data quality testing gates, and infrastructure-as-code.
Define architectural patterns for streaming and event-driven technologies to support high-velocity enterprise and IoT telemetry data.
Elicit detailed business and architecture requirements, translating them into clear architectural guidelines and actionable work items for data engineering teams.