Data Management Analyst​/Senior Data Management Analyst

data management analyst / senior data management analyst

join to apply for the data management analyst / senior data management analyst role at hormel foods

$86,500.00/yr - $/yr

data management analyst / senior data management analyst – order to cash and finance – information technology services – hormel foods corporate office (austin, mn, willmar, mn, eden prairie, mn or naperville, il)

to save time applying, hormel foods does not offer sponsorship of job applicants for employment-based visas for this position at this time.

hormel foods corporation

hormel foods corporation, based in austin, minnesota, is a global branded food company with approximately $12 billion in annual revenue across more than 80 countries worldwide. Its brands include planters®, skippy®, spam®, hormel® natural choice®, applegate®, justin’s®, wholly®, hormel® black label®, columbus®, jennie-o® and more than 30 other beloved brands. The company is a member of the s&p 500 index and the s&p 500 dividend aristocrats, was named one of the best companies to work for by u.s. News & world report, one of america’s most responsible companies by newsweek, recognized by time magazine as one of the world’s best companies, and has received numerous other awards and accolades for its corporate responsibility and community service efforts.

The company lives by its purpose statement — inspired people. Inspired food.™ — to bring some of the world’s most trusted and iconic brands to tables across the globe. For more information, visit

this position is responsible for ensuring data accuracy, consistency, and quality across the organization with a primary focus on order to cash and finance data and processes. The ideal candidate will have a strong process and analytical mindset, a deep understanding of data management principles, and the ability to drive data and process initiatives. The ideal candidate will have functional and technical experience order to cash and finance data and processes in manufacturing or cpg organizations.

Key functional areas may include, but are not limited to:

• order management & fulfillment, transportation, invoicing, accounts receivable, pricing
• costing, general ledger & chart of accounts, accounts payable & vendor data, financial reporting & compliance, treasury & cash management

data stewardship and ownership

• collaborate with data stewards and data owners to define and document business data rules and data quality rules.
• align data quality rules and definitional information with data owners and stewards to ensure consistency and accountability.
• perform data profiling to understand data characteristics and identify anomalies.
• partner with data stewards to remediate data issues and improve data quality in source systems.
• implement data quality standards, procedures, and validation & cleansing rules.
• develop and implement data quality metrics and automated monitoring processes.
• leverage data management tools to profile and analyze data at aggregate and granular levels to derive insights and actions.

data governance and strategy

• contribute to the development and implementation of data governance policies, standards, and procedures.
• support the key pillars of the data quality program: data definitions, ownership/stewardship identification, and business data quality rules.
• collaborate with key stakeholders to understand data requirements and ensure alignment with governance frameworks.

project and change management

• lead and support data-related projects across experience order to cash and finance domains.
• coordinate activities among project teams and data governance working groups for data quality definition and remediation and process enhancements.
• apply project management skills to drive initiatives from planning through execution.

strategic leadership in data quality

• recommend and lead process and data improvement activities across business areas and it teams.
• communicate master data management (mdm) strategies, policies, and initiatives to stakeholders at all levels.
• identify, analyze, and resolve data quality issues across assigned data domains.

tooling and technology

• leverage tools and technologies to support data…