Product Data Analyst

About the role

NEFCO is expanding beyond a distribution network and aiming to shape the next generation of construction supply. As the business moves from $1 billion toward $5 billion in growth, strong product data is essential for delivering excellent customer experiences, running operations efficiently, and supporting scale.

The Product Data Analyst role is ideal for someone who enjoys detailed data work, analytical problem-solving, and using information as a business advantage. You will support the upkeep, enhancement, and optimization of product data across multiple systems while contributing to automation, stronger data quality, and enterprise-wide governance efforts.

If you like tackling complex data issues, working with teams across the organization, and creating processes that can grow with the business, this role may be a great fit.

Product data management

• Keep product records current by enriching key details such as descriptions, specifications, classifications, images, dimensions, and identifiers like UNSPSC, GTIN, and MPN across ERP, PIM, and related platforms.
• Find, verify, and fill in missing product information using suppliers, manufacturers, and third-party data sources.
• Support onboarding of new products by checking, validating, and enriching data before it moves into downstream systems.
• Maintain alignment so product information stays accurate and consistent across all systems and channels.

Data quality and governance

• Apply data quality rules and carry out regular reviews to uncover gaps, inconsistencies, duplicates, and compliance concerns.
• Track and improve completeness, accuracy, and consistency through established governance practices.
• Create and maintain dashboards and reports that measure data quality KPIs, enrichment status, and operational performance.
• Help define best practices, accountability models, and documentation for product data processes.

Process improvement and automation

• Use SQL, Excel, Power Query, ETL tools, and AI-assisted tools to automate data preparation, validation, and ongoing maintenance tasks.
• Look for ways to reduce repetitive manual work and improve scalability through workflow automation.
• Support ERP, PIM, and master data management projects that improve efficiency and data reliability.
• Assist with supplier integrations, catalog syndication, and product content distribution efforts.

Cross-functional collaboration

• Work with Pricing, Supply Chain, Sales, Operations, Marketing, and IT teams to understand needs and translate them into practical data solutions.
• Act as a go-to resource for product data knowledge across the organization.
• Train users on data standards, self-service reporting tools, and recommended practices.

Qualifications

• A bachelor’s degree in Business, Information Systems, Data Analytics, Computer Science, or a similar field; equivalent experience may also be considered.
• 2 to 4 years of experience in product data management, master data management, catalog operations, analytics, or a related area.
• Strong SQL capability and advanced Excel skills, including Pivot Tables, Power Query, lookups, and data validation methods.
• Hands-on experience with ERP, PIM, or other enterprise data management systems.
• Working knowledge of ETL processes, data transformation, and workflow automation concepts.
• Strong analytical thinking, troubleshooting ability, and attention to detail.
• Ability to juggle multiple priorities and deliver reliable work in a fast-moving environment.
• Clear communication skills and the ability to work well with both technical and non-technical stakeholders.
• Experience with business intelligence tools such as Power BI, Tableau, or similar platforms.
• Exposure to Python or other scripting languages for automation and data handling.
• Experience with APIs, ETL pipelines, FTP/SFTP integrations, or other data exchange technologies.
• Familiarity with Epicor Eclipse, Salsify, or comparable ERP/PIM solutions.
• Background in industrial distribution, manufacturing, construction supply, or wholesale settings is preferred.
• Exposure to AI-driven data enrichment tools, machine learning, or large language models is an advantage.
• Understanding of product taxonomy, classification systems, and data governance frameworks.

What success looks like

• Product information is complete, accurate, consistent, and easy to access.
• Data issues are identified early and addressed through scalable processes.
• Automation cuts down manual work and improves efficiency.
• New product onboarding becomes standardized, streamlined, and repeatable.
• Teams across the company trust the accuracy and reliability of product data.
• Data governance practices are clearly defined, widely adopted, and maintained over time.