Senior Principal Enterprise Data Architect, AI Data Transformation

Achievements

The Senior Principal Enterprise Data Architect – AI Data Transformation role combines advanced enterprise data architecture with deep expertise in Artificial Intelligence infrastructure and Data Science enablement. This role involves planning, designing, deploying, and executing technology solutions aligned with the organization's strategic roadmap.

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.
• 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.
• 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).
• 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.

Qualifications

• Bachelor's degree in Computer Science, Data Science, Information Systems, Mathematics, Engineering, or a related technical field required.
• Master's degree in Computer Science, Data Science, Artificial Intelligence, or a related field strongly preferred.
• 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.
• Prior experience architecting data solutions involving complex supply chains, ERP (SAP/Oracle), PLM, or large-scale IoT/telemetry is preferred.
• Excellent oral and written presentation skills.
• Works independently with limited supervision and operates autonomously.
• Working knowledge of enterprise architecture frameworks (e.g., TOGAF, Zachman).