Head of Enterprise Data & Architecture

Position Overview

The Head of Enterprise Data & Architecture is an executive enterprise leader responsible for architecting the company’s future-state data foundation—enabling automation, advanced analytics, AI, and scalable digital transformation across Finance, HR, Procurement, FP&A, Operations, and Reporting. This role owns the enterprise data strategy, designs a unified data architecture, modernizes foundational financial structures, and establishes governance, lineage, and integration frameworks that ensure real-time, trusted, intelligent data flows across the corporate ecosystem. The leader is accountable for designing next-generation data capabilities, remediating legacy complexity, and ensuring the enterprise is architecturally positioned to leverage automation, AI, and agentic solutions at scale.

Key Responsibilities

• Enterprise Data Strategy & AI-Ready Architecture
  • Define and execute the future-state enterprise data strategy, focused on automation, interoperability, machine-readable structures, and AI enablement.
  • Design and own the enterprise data model across all systems, ensuring it is optimized for real-time insights, predictive analytics, and scalable automation.
  • Architect cloud-first, modular, intelligent data platforms that support high-volume ingestion, transformation, and consumption across planning, reporting, and operational workflows.
  • Establish forward-looking data standards that ensure system designs are AI-ready, lineage-aware, and automation-friendly.
• Modernization of Core Financial & Operational Structures
  • Rebuild the chart of accounts, legal entity hierarchy, and financial dimensions with a forward-looking design that supports global expansion, multi-scenario modeling, automation, and self-service analytics.
  • Define a unified global capability model across Finance, HR, Procurement, and Operations, ensuring cross-system constructs are consistent, reusable, and machine-actionable.
  • Develop scalable transformation rules, metadata models, and canonical structures to streamline future integrations and new-system adoption.
• Intelligent Integration Architecture & Autonomous Data Flows
  • Architect end-to-end integrations that support automated, intelligent data flow across core platforms, including:
    • Workday → D365 → OneStream → Power BI
  • Implement event-driven, API-led, and streaming integration patterns that improve timeliness, reduce latency, and enable autonomous reconciliation.
  • Establish global mapping rules, validation logic, and lineage requirements enforced through automated controls and monitoring.
  • Ensure integration frameworks support predictive exception handling, ML-driven anomaly detection, and automated quality remediation.
• Enterprise Data Governance for an AI-Driven Organization
  • Own the enterprise data governance framework, ensuring it evolves to support advanced analytics, GenAI, agentic workflows, and automated decisioning.
  • Implement automated data quality checks, lineage intelligence, metadata-driven orchestration, and continuous monitoring.
  • Establish enterprise-wide rules for access, classification, privacy, and usage to ensure data is trustworthy, secured, and compliant for both human and AI consumption.
  • Drive enterprise-wide adoption of stewardship practices supported by intelligent tooling.
• Legacy Remediation & Continuous Modernization
  • Lead the uplift of existing systems by remediating legacy design flaws, removing structural inconsistencies, and eliminating manual workarounds.
  • Replace brittle processes with automated pipelines, governed integrations, and reusable architectural patterns.
  • Create a modernization blueprint that accelerates autonomy, resilience, and scalability across the entire data and systems state.
• Enterprise Leadership & Vision Setting
  • Partner with Finance, HR, Operations, Technology, and business leaders to develop a unified, automation-centric vision for data and architecture.
  • Influence executive decision-making by articulating a future-state architecture that reduces operational friction and unlocks AI-enabled insights.
  • Champion a culture of innovative thinking, intelligent automation, continuous optimization, and data-driven decisioning.
  • Coach and develop architects and technical leaders to adopt modern architectural methods, metadata-driven design, and AI-first thinking.
• Value Realization Through Automation & AI Enablement
  • Define KPIs that measure progress toward an autonomous and AI-ready enterprise, reducing manual effort, increasing reliability, and accelerating insights.
  • Drive material improvements in financial close speed, reporting accuracy, mapping precision, and system reliability through automation and re-architecture.
  • Enable predictive analytics, proactive exception detection, and AI-driven scenario modeling through well-designed data structures and intelligent workflows.
  • Deliver a unified architectural foundation that significantly enhances productivity, reduces cost, and improves operational agility.

Required Qualifications

• 20+ years of experience in enterprise data architecture, integration, or corporate systems architecture, preferably in global financial or information-driven organizations.
• Proven ability to design future-state data architectures, automate workflows, and enable AI/ML across core enterprise functions.
• Deep expertise in enterprise financial structures, data modeling, metadata management, lineage design, master data, and semantic layer development.
• Significant experience with Workday, D365, OneStream, Power BI, and cloud-based integration frameworks.
• Demonstrated success modernizing legacy systems, redesigning foundational structures, and implementing automation-first architectures.
• Strong capability in cloud-native engineering, API-led integration, event-driven architecture, and metadata-driven automation.
• Outstanding communication and executive influence skills with the ability to translate complex architectural vision into business outcomes.