Staff Data Architect

The Staff Data Architect is part of GE Vernova Enterprise Analytics and plays a critical leadership role in designing and governing enterprise-scale data architectures that enable analytics, AI, and GenAI solutions. This role supports the GEV Enterprise and Head Quarters domains/functions by ensuring data is well-modeled, trusted, scalable, and AI-ready. Reporting to the Enterprise/HQ Analytics and AI Leader (or Data Architecture Leader), the Staff Data Architect partners closely with analytics product managers, data engineering, AI/ML/GenAI teams, and business stakeholders. This role owns the end-to-end data architecture, from source systems through curated layers, enabling advanced analytics, operational reporting, and AI-driven insights., * Define and own enterprise data architecture standards, patterns, and best practices aligned with GE Vernova's analytics and AI strategy.

• Lead conceptual, logical, and physical data modeling across key enterprise domains, including:

• Finance (GL, FP&A, cost, profitability)
• Sourcing & Procurement
• Treasury & Cash Management
• Supply Chain & Logistics

• Translate complex business processes into reusable, governed, and scalable data models.

Data Modeling & AI-Ready Data Design

• Design analytics-optimized and AI-ready data models, including dimensional, data vault, and lakehouse patterns.
• Ensure data structures support:

• Business intelligence and advanced analytics
• Machine learning and GenAI use cases
• Feature engineering and model lifecycle needs

• Partner with AI/ML teams to ensure data is fit-for-purpose for predictive, prescriptive, and generative solutions.

Platform & Technology Leadership

• Architect and guide solutions on the Databricks Lakehouse platform, including:

• Bronze, Silver, and Gold data layers
• Unity Catalog and enterprise data governance
• Performance, scalability, and cost optimization

• Collaborate with cloud and platform teams to ensure architectures are secure, resilient, and compliant.
• Evaluate and influence adoption of emerging analytics, AI, and GenAI technologies.

Source Systems & Integration

• Analyze and document source application data models (ERP, CRM, PLM, TMS, WMS, Finance systems).
• Define integration and data pipeline patterns that ensure data quality, lineage, and traceability.
• Partner with data engineering teams to guide ingestion, transformation, and orchestration strategies.

Governance, Quality & Stewardship

• Embed data governance, metadata, master data alignment, and lineage into all architectural designs.
• Establish standards for data quality, consistency, security, and regulatory compliance.
• Act as an architectural authority and data steward, reviewing and approving designs across programs.

Leadership & Collaboration

• Serve as a technical thought leader and mentor for architects, engineers, and analytics teams.
• Collaborate with Analytics Product Managers to align architecture with business roadmaps and priorities.
• Communicate architectural decisions clearly to technical and non-technical audiences.
• Influence prioritization, architectural trade-offs, and long-term platform strategy.

• Bachelor's degree in Computer Science, Engineering, Data, or other STEM disciplines.
• 10+ years of experience in data architecture, data modeling, or enterprise analytics platforms.
• Deep expertise in data modeling across finance, sourcing, treasury, logistics, and operations domains.
• Strong understanding of ERP, CRM, PLM, and finance system data structures.
• Hands-on experience with Databricks and modern lakehouse architectures.
• Proven experience designing AI/ML- and GenAI-ready data solutions.
• Experience with cloud data platforms (Azure preferred; AWS/GCP acceptable).
• Strong knowledge of data governance, metadata, data quality, and security.
• Excellent communication skills with the ability to translate complex data concepts into business-aligned outcomes.
• Demonstrated leadership and influence across cross-functional teams.

Preferred Qualifications

• Master's degree in a relevant technical or analytics field.
• Experience supporting enterprise-scale AI, ML, or GenAI initiatives.
• Familiarity with data mesh, data fabric, or domain-oriented architecture.
• Experience working in agile, product-based delivery models.
• Relevant cloud, data, or analytics certifications.