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Description

We are seeking a principal-level (L7) leader to own and drive the Enterprise Data Strategy, AI, and
New Datamart vision across our ERP-centric technology landscape. This role spans enterprise
platforms including SAP S/4HANA, Oracle Cloud Applications and similar ERP ecosystems leveraging
modern data platforms such as SAP Datasphere, Oracle Analytics Cloud, Databricks, Amazon
Redshift, or equivalent to build a unified, intelligent data fabric.
This leader will serve as the strategic bridge between enterprise data architecture, advanced
analytics, and business intelligence — translating complex, multi-Enterprise application data
landscapes into actionable, AI-powered insights at scale. The ideal candidate is platform-fluent but
platform-agnostic, able to design data strategies that harness the best of any enterprise application
ecosystem while maintaining a clean, governed, and extensible data foundation.
A critical mandate of this role is to establish and chair a Data Governance Council within
Manufacturing Operations, driving cross-functional alignment across Engineering, Manufacturing,
Supply Chain, and Finance to ensure data integrity, standardization, and actionable intelligence
throughout the manufacturing value chain.

Key job responsibilities
Enterprise Data Strategy & Datamart Architecture
• Define and own the enterprise data strategy across ERP-centric environments (SAP, Oracle,
or similar), establishing the roadmap for modernizing legacy data warehouses into cloud-
native datamarts.
• Design and implement a new Datamart architecture leveraging platforms such as SAP
Datasphere, Amazon Redshift, Aurora, unifying ERP and non-ERP data through virtualization,
replication, or hybrid approaches.
• Establish the semantic layer and business data fabric that preserves business context across
disparate enterprise systems, enabling consistent metrics, KPIs, and definitions across all
functional domains (Finance, Supply Chain, Manufacturing, Engineering, Order-to-Cash,
Procurement).
• Lead Data Modeling & Semantic Layer design — defining reusable business terms, metrics,
relationships, and associations that support analytics, planning, and AI/ML initiatives
regardless of the underlying ERP platform.
• Architect cloud data warehousing, data marts, and data pipelines & orchestration to
ensure scalable, performant, and governed data delivery from multiple ERP sources.

Own Data Quality & Governance frameworks ensuring data integrity, lineage, certification,
and lifecycle management across all enterprise datamarts and ERP systems — with
particular emphasis on Manufacturing Operations data standards.
Manufacturing Operations Data Governance Council
This role is accountable for chartering, establishing, and chairing a Data Governance Council within
Manufacturing Operations. The council will drive cross-functional data alignment and decision-
making across key operational domains:
• Charter and launch the Manufacturing Operations Data Governance Council, defining its
mission, scope, membership, decision rights, escalation paths, and cadence of reviews.
• Collaborate with Engineering to standardize product data definitions, BOM structures,
engineering change order data flows, and design-to-manufacturing data handoffs.
• Partner with Manufacturing to govern production data (MES, quality, yield, OEE), enforce
data standards across shop-floor systems, and enable real-time manufacturing analytics.
• Align with Supply Chain on demand planning data, inventory master data, logistics and
fulfillment metrics, and end-to-end supply chain visibility through governed data pipelines.
• Coordinate with Finance to ensure manufacturing cost data, variance analysis, standard
costing, and COGS reporting are underpinned by trusted, governed data from operational
systems.
• Define and enforce cross-functional data standards, data ownership models, stewardship
roles, and data quality SLAs across all council-participating functions.
• Establish a data issue resolution framework with clear escalation paths and accountability,
ensuring disputes over data definitions, ownership, and quality are resolved efficiently.
• Report governance health metrics to senior leadership, including data quality scorecards,
policy compliance rates, and council effectiveness KPIs.
Enterprise Reporting & Analytics
• Set the strategic direction for ERP-native reporting capabilities — including SAP Embedded
Analytics (Fiori), Oracle OTBI/BI Publisher, or equivalent built-in reporting tools.
• Drive the evolution of Enterprise Analytics & BI leveraging platforms such as SAP Analytics
Cloud (SAC), Oracle Analytics Cloud (OAC), Amazon QuickSight, Tableau, or Power BI for
dashboards, scorecards, planning, predictive analytics, and self-service analytics.
• Define and implement KPI Frameworks and Data Visualization standards, enabling insight-
to-action workflows across the enterprise independent of the source ERP system.
• Champion User Enablement & Adoption designing programs that democratize data access
and empower business users with self-service analytics capabilities across all ERP platforms.
• Standardize operational reporting, ad-hoc reporting, and embedded analytics patterns that
work consistently whether the source system is SAP, Oracle, or a third-party application.
AI & Advanced Analytics
• Identify, prioritize, and deliver Generative AI Use Cases for Enterprise Applications
leveraging AI services (e.g., Amazon Bedrock, Amazon Q, SAP Joule, Oracle AI, Azure OpenAI)
to embed intelligence into ERP-driven business processes.
• Build and scale Machine Learning Models for demand forecasting, supply chain
optimization, financial planning, anomaly detection, and process automation across ERP
platforms.

Drive Forecasting & Optimization initiatives that convert historical ERP data (from SAP,
Oracle, or similar) into predictive and prescriptive insights.
• Lead Intelligent Automation efforts automating repetitive data tasks, report generation, and
exception-based alerting through AI-powered workflows integrated with enterprise
applications.
• Establish AI governance frameworks ensuring responsible, compliant, and explainable AI
across all analytics use cases, regardless of the underlying ERP or data platform.
Integration & Platform Evolution
• Partner with Architecture & Governance teams to ensure alignment with clean core
strategy, extension strategies (e.g., SAP BTP, Oracle Cloud Infrastructure, AWS), and
integration standards.
• Collaborate with integration teams for data orchestration across cloud and on-premises ERP
systems — managing API gateways, event-driven architectures, and ETL/ELT pipelines.
• Drive platform evolution toward modern data architectures such as SAP Business Data
Cloud, Oracle Lakehouse, AWS Data Lake, Databricks Lakehouse — evaluating and road
mapping the best-fit architecture for the enterprise.
• Interface with Process & Business Excellence to translate business demand into data
solutions, ensuring tight alignment between business requirements and data architecture
decisions across all ERP systems.