Staff Data Architect

GE Vernova • Full-time • Bengaluru • 5d ago

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.