The J.R. Simplot Company is a diverse, privately held global food and agriculture company headquartered in Boise, Idaho. We are a true farm-to-table company with an integrated portfolio including food processing and food brands, phosphate mining, fertilizer manufacturing, farming, ranching and cattle production, and other enterprises related to agriculture.

SUMMARY

Through basic data profiling, standardization, and data quality monitoring, the SAP Data Quality Engineer serves acritical role in maximizing the utility of Simplot’s master data. The Engineer must have a deep curiosity forexploring how master data is used across the company, and a drive to improve the quality of the data to supportoperational efficiencies and complex decision making. This role requires broad business acumen coupled with astrong technical skill set for transforming data.
The SAP Data Quality Engineer will establish strong partnerships with business stakeholders and capture detailedrequirements to enable effective engagement in projects, stakeholder meetings, and end user processes. Maintaining on-going awareness of changing business needs and systems as they relate to master data usage isessential. Once standardized master data is in place, the Engineer will propose and work with Data Quality Leads to develop reporting and tools to actively monitor the data to ensure the standards are upheld.

TYPICAL EDUCATION

Bachelor’s Degree (B.A. or B.S.) from 4 year college or university

RELEVANT EXPERIENCE

• 3+ years of industry relevant job experience preferrably in product manufacturing and/or supply chain
• 3+ years experience in data quality roles, with specific experience in SAP environments
• Skilled at using SQL scripts to analyze and transform data
• Advanced Microsoft Excel skills
• Power BI experience a plus
• Excellent interpersonal and communication skills with the ability to cultivate professional business partnerships
• Highly skilled at facilitation and building consensus among diverse groups of stakeholders
• Track record of dealing well with ambiguity, prioritizing needs, planning and managing multiple projects, and delivering measurable results in a fast-paced environment
• Demonstrated partnering skills with both technical and non-technical partners, including senior leadership
• Experience (and preferably enjoyment of) balancing numerous priorities and demands
• Self-motivated and results-oriented
• Experience using Profisee MDM is preferred
• Willingness to travel up to 10% (may include international travel)

• Participate in meetings with stakeholders to map data attributes from source to target systems to enable standardization and system to system conversions.
• Build a thorough understanding of the use case for each in-scope attribute to ensure all business rules are captured. Work with stakeholders to simplify attributes where multiple use cases exist to create enterprise alignment.
• Profile data to ensure each source to target attribute is properly mapped based on its definition and use case. Additionally, understand the extent to which each attribute is standardized (e.g., naming conventions, value formats, domain of values, etc.), accurate, complete, and unique (e.g., duplicates, records not accurately categorized).
• Record and communicate observations made during profiling to a Data Lead. Develop action plans in collaboration with a Data Lead to address the requirements and issues identified.
• Identify and propose automated data transformations that conform source system data to meet the standardization requirements in the target system. Partner with business users for manual transformation methods when automation is not possible.
• Partner closely with business and IT to ensure adequate testing and validation is conducted to ensure the data is complete and accurate before loading into production environments.
• Applies growing practical and technical experience to implement data standardization, including business rules, data validation, and matching strategies, using a master data management platform.
• Partner with Enterprise Data Governance to define data quality metrics relevant to each master data domain.
• Collaborate on reporting solutions to actively monitor data quality, and construct workflows to engage data stewards in correcting data quality issues in a timely manner.
• For approved data quality improvements requiring substantial resources, partner with a Data Quality Lead to develop a project plan that implements the changes in an agreed upon timeframe.