Techno-Functional Consultant - Finance Data & KPI Standardization; Footwear, Apparel, Retail NC

This role defines the business and data requirements that make a multi-ERP, multi-region data platform trusted by Finance. You will work with the CFO’s organization and the technical delivery team to turn finance questions into certified KPI definitions, harmonized master data, and a semantic layer that supports natural-language queries.

• Lead workshops with Finance, Supply Chain, and Sales leaders to define and certify KPIs such as cost of goods sold, gross margin, inventory turnover, operating margin, and cash conversion.
• Document KPI definitions, calculation logic, ownership, and the differences between ERPs and regions.
• Define master data standards for product (brand, line, style, SKU), location, and customer, including the mapping rules from each source ERP.
• Write the semantic layer requirements: business entities, metrics, hierarchies, currency handling, and the access and security rules.
• Build a prioritized backlog and acceptance criteria, and work with data engineers to validate that what is built matches the definitions.
• Identify where AI can speed up the work, for example drafting definitions from ERP metadata, proposing hierarchy mappings, and generating data quality checks. Review and approve the output.
• Support Finance stakeholders through sign-off and adoption.

• 10+ years in finance, supply chain, or data consulting, with at least 5 in retail, footwear, apparel, or consumer goods.
• Hands-on experience with at least two ERPs (for example SAP, Oracle, or Microsoft Dynamics) and their finance and inventory data models.
• Proven delivery of KPI standardization or master data programs across multiple business units or countries.
• Working knowledge of a lakehouse or cloud data platform (Databricks preferred), and of semantic or metrics layers and BI tools.
• Strong facilitation skills with CFO-level and senior Finance stakeholders, and a clear writing style for requirements and definitions.

• Experience with natural-language or generative AI analytics on enterprise data.
• Familiarity with data governance and cataloging tools.
• A finance qualification (CPA, CMA, or equivalent).