Job Description Summary
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.
Job Description
Enterprise & Domain Data Architecture
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.
Required Skills and Qualifications
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
.
Additional Information
Relocation Assistance Provided:
Yes
