Product Data Operations Analyst

Description NEFCO is scaling from $1B to $5B, and high-quality product data is foundational to that growth. We are looking for a detail-oriented, technically curious Product Data Operations Analyst to maintain, enrich, and improve product data quality across ERP, PIM, supplier, and analytics systems — while identifying opportunities to automate workflows and strengthen data governance. What You'll Do Maintain and enrich product data attributes — descriptions, specs, classifications, images, and identifiers (UNSPSC, GTIN, MPN) — across ERP and PIM systems. Source missing attributes from suppliers, manufacturers, and third-party databases. Support new product onboarding: review, validate, and enrich data before loading into downstream systems. Apply data quality standards and run regular audits to catch gaps, inconsistencies, and duplicates. Build and maintain dashboards tracking data quality KPIs, completeness scores, and enrichment progress. Use SQL, Excel, Power Query, ETL tools, and AI-assisted scripting to automate data prep and validation workflows. Partner with Pricing, Sales, Supply Chain, IT, and Operations to translate business needs into data requirements. Support ERP/PIM improvement initiatives; assist with supplier portals, catalog syndication, and compliance projects. Train end-users on data best practices and self-service tools. Qualifications Bachelor’s degree in business, Information Systems, Data Analytics, or related field (or equivalent experience). 2–4 years in product data, master data, PIM/ERP data management, catalog operations, or a related analytics role. Solid SQL skills and strong Excel proficiency (pivot tables, Power Query, lookups, data validation). Working knowledge of ETL concepts and comfort with scripting, automation, or AI-assisted data tools. Experience with ERP or PIM systems; Epicor Eclipse or Salsify a plus. Detail-oriented, organized, and able to manage competing priorities. Clear communicator with both technical and non-technical stakeholders. Power BI, Tableau, or similar BI tools. Python for data manipulation and automation. Experience with ETL pipelines, APIs, or FTP/SFTP data exchanges. Background in industrial distribution, manufacturing, or wholesale. Exposure to AI-assisted enrichment tools or LLMs for classification and content generation. Familiarity with data governance, taxonomy management, or product classification. What Success Looks Like Product data is more complete, accurate, and easier for teams to use. Data quality issues are discovered early and resolved through repeatable processes. Manual cleanup is accomplished through automation, ETL workflows, and better source collection. Product onboarding is consistent and less reliant on tribal knowledge. Stakeholders trust the data because standards and ownership are clearly defined. #J-18808-Ljbffr NEFCO