Confidential
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. Architect end-to-end integrations that support automated, intelligent data flow across core platforms, including: 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. 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. Ensure future implementations adopt best-in-class design principles aligned to automation, AI, and low- or no-touch operations. 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 Skills & Experience
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.
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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. Architect end-to-end integrations that support automated, intelligent data flow across core platforms, including: 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. 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. Ensure future implementations adopt best-in-class design principles aligned to automation, AI, and low- or no-touch operations. 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 Skills & Experience
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.
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