Enterprise Data Architect(Data, AI & Analytics)

About the Role

We are looking for a highly experienced Enterprise Data Architect to establish and evolve the enterprise data foundation into a data-driven, AI-enabled enterprise .

This role will lead the design of our enterprise data architecture using Microsoft Fabric, Power BI, Azure and modern data modeling practices , with a strong focus on connecting business processes, operational systems and financial data into a common enterprise model.

The architect will translate complex business processes—including Order-to-Cash (O2C), Procure-to-Pay (P2P), Inventory, Record-to-Report (R2R), Pricing, Customer and Product —into scalable enterprise data models that enable trusted reporting, advanced analytics, AI and intelligent agents.

The ideal candidate combines deep data architecture expertise with strong business process knowledge and can work directly with business leaders to turn data into actionable insights and automation.

Key Responsibilities

1. Enterprise Data Architecture

• Define and maintain the enterprise data architecture, standards, principles and roadmap.
• Design scalable data models across operational, analytical and financial domains.
• Establish a common enterprise semantic model and business definitions for critical metrics and KPIs.
• Develop data architecture standards covering data ingestion, transformation, modeling, governance, security and consumption.
• Create a logical enterprise data model spanning customers, products, suppliers, orders, inventory, shipments, invoices, payments and financial transactions.
• Ensure the architecture supports both current business needs and future AI/agent capabilities.

2. Business Process Data Modeling

Lead the development of enterprise data models for key business processes:

Order-to-Cash (O2C)

• Customer → Quote → Order → Allocation → Shipment → Invoice → Payment
• Order fulfillment, backlog, OTIF, revenue, margin and collections analytics.

Procure-to-Pay (P2P)

• Supplier → Requisition → PO → Receipt → Invoice → Payment
• Supplier performance, purchasing spend, AP, three-way match and working capital.

Inventory

• Product → Location → Inventory → Movement → Allocation → Shipment
• Inventory availability, turns, aging, excess & obsolete, service levels and working capital.

Record-to-Report (R2R)

• Transaction → Subledger → Journal → GL → Financial Statement
• Revenue, COGS, gross margin, working capital, profitability and management reporting.

3. Microsoft Fabric Architecture

Own the enterprise data architecture leveraging Microsoft Fabric , including:

• OneLake architecture and enterprise data organization.
• Lakehouse and Warehouse design.
• Data Factory / pipelines and orchestration.
• Dataflows and transformation architecture.
• Fabric semantic models.
• Power BI enterprise architecture.
• Real-time analytics where appropriate.
• Data security including row level security implementation.
• Integration with ERP, CRM, WMS, TMS, e-commerce and other enterprise applications.
• Development of reusable data products and curated datasets.

Establish appropriate patterns for Bronze / Silver / Gold data layers and ensure data products are optimized for performance, scalability and usability.

4. Data & AI Enablement

Design the data foundation required to enable AI, GenAI and intelligent agents across the enterprise.

Responsibilities include:

• Identify high-value AI and agent opportunities across O2C, P2P, inventory, finance and customer operations.
• Ensure enterprise data is structured, governed and accessible for AI consumption.
• Design data architectures supporting natural-language analytics and conversational BI.
• Enable AI-powered decision support, forecasting, anomaly detection and recommendations.
• Partner with business and IT teams to develop AI agents that can reason across enterprise data and execute business workflows.
• Evaluate emerging Microsoft AI capabilities, including Copilot, Fabric AI capabilities and Azure AI .
• Establish appropriate data, security and governance controls for AI use cases.

5. Data Governance & Quality

• Establish enterprise data governance standards and ownership.
• Define data ownership, stewardship and accountability by domain.
• Establish critical data elements and data quality rules.
• Develop data lineage and metadata standards.
• Ensure appropriate security, privacy and access controls.
• Part…