Job Description

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.

Requirements

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.