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# Data Quality Engineer III

- **Company:** Central Michigan University
- **Location:** Mount Pleasant, MI, United States
- **Experience:** Expert
- **Salary:** $60,000 - $85,000
- **Contract:** Temporary contract
- **Skills:** Data Analysis, Cloud Computing, Cyber Security, Data as a Services, Data Validation, Data Cleansing, Data Conversion, Data Files, Data Governance, Data Integration, Extract Transform Load (ETL), Data Migration, Data Profiling, Data Recovery, Data Security, Data Warehousing, Relational Databases, Electronic Data Interchange (EDI), Data Flow Control, Python (Programming Language), Microsoft SQL Server, Oracle (Applications), Windows PowerShell, Release Management, Application Data, SAP (Applications), Software Deployment, Software Systems, SQL Stored Procedures, SQL Databases, Systems Integration, Technical Data Management Systems, Strategies of Testing, Enterprise Data Management, Data Processing, Scripting, Enterprise Software Applications, Integration Frameworks, Data Management, Tools for Reporting, Workday
- **Published:** November 3, 2026
- **Apply:** https://www.jobs.cmich.edu/postings/45708/pre_apply

## About the Role

Data Quality Engineer I:
Foundational knowledge of data analysis, reporting, data quality, or enterprise data concepts.
Familiarity with relational databases, SQL, spreadsheets, reporting tools, or data extraction methods.
Ability to troubleshoot routine data issues, follow established procedures, and learn institutional systems and data processes.
Strong attention to detail, communication, customer service, and collaboration skills.

Data Quality Engineer II:
All the qualifications of Data Quality Engineer I, plus:
Four years of experience, or equivalent combination of education and experience, related to data analysis, data quality, reporting, data migration, data integration, or enterprise systems.
Experience supporting data profiling, validation, cleansing, reconciliation, reporting, or data conversion activities.
Experience with enterprise data support technologies such as SQL databases, reporting tools, ETL or data integration tools, data warehouses, dashboards, or enterprise application data services.
Ability to recommend and implement improvements to data validation, data remediation, documentation, or operational practices.
Working knowledge of data quality rules, source-to-target mapping, validation logic, and ongoing data management processes.
Experience with data issue analysis, root-cause investigation, troubleshooting, or performance support.
Strong analytical, problem-solving, and collaboration skills., Six years of experience, or equivalent combination of education and experience, related to data analysis, data quality, reporting, data migration, data integration, or enterprise systems.
Experience contributing to data quality planning, validation strategies, remediation approaches, or operational improvement efforts for enterprise data and related systems.
Strong working knowledge of relational data structures, SQL, reporting, ETL, integration, or data warehouse technologies used to support enterprise data quality work.
Strong working knowledge of relational data structures, SQL, reporting, ETL, integration, or data warehouse technologies that support enterprise data quality work.
Experience working with ERP, cloud-hosted, vendor-managed, or hybrid application data environments.
Ability to mentor, support, or share knowledge with other staff.
Experience supporting integrations or data exchange activities across multiple systems.
Ability to contribute to the design, improvement, or long-term support of enterprise data quality solutions.
Excellent communication, collaboration, and problem-solving skills., Eight years of experience, or an equivalent combination of education and experience, related to data analysis, data quality, reporting, data migration, data integration, or enterprise systems.
Experience leading data quality, data conversion, reconciliation, remediation, or enterprise data management work across multiple complex data domains or services.
Strong understanding of data governance, data stewardship, security practices, risk mitigation, and operational reliability in enterprise data environments.
Ability to lead or significantly contribute to the design, scalability, resiliency, and long-term support of enterprise data quality solutions, validation processes, and reconciliation practices.
Demonstrated ability to identify improvement opportunities and develop practical recommendations for implementation.
Demonstrated professionalism and sound judgment in working with faculty, staff, students, vendors, implementation partners, and other stakeholders.
Demonstrated leadership in communication, coordination, or stakeholder engagement for complex data quality work.
Models collaborative, service-oriented, and values-based behavior in support of team and university goals.
Preferred Qualifications
Experience with enterprise platforms and technologies such as SAP, Oracle, Workday, SQL Server, data warehouses, business intelligence platforms, ETL or integration tools, cloud-hosted systems, or similar tools.
Familiarity with scripting or automation tools, such as Python, PowerShell, SQL stored procedures, or similar technologies, to support routine data quality or validation tasks.
Experience partnering with business units, user offices, data stewards, subject matter experts, or implementation partners to identify needs and develop solutions using supported data and systems.
Ability to gather, analyze, and document user needs, data rules, business requirements, validation criteria, or source-to-target mapping information to support data improvements or implementations.
Experience working in technical support, reporting, data services, application support, service desk, desktop support, or other customer-facing IT roles.

## Description

The Data Quality Engineer supports institutional data quality across Central Michigan University by profiling, validating, cleansing, converting, reconciling, and improving data used by enterprise systems, reporting, compliance, decision-making, and ERP migration efforts. The position develops and applies data quality rules, validates source-to-target mappings, documents findings, tracks issues, troubleshoots data problems, and coordinates with functional and technical partners to improve the accuracy, completeness, consistency, reliability, security, and usability of institutional data. The position will support ERP migration, operational data quality, reporting, integration, and ongoing data governance activities across student, finance, human resources, payroll, procurement, research administration, and other administrative data domains., Data Quality Engineer I:
Supports assigned data sets by resolving routine data quality issues and building working knowledge of data definitions, source systems, dependencies, and change impacts.
Uses established procedures to profile data, validate results, document findings, and escalate issues requiring additional analysis or decision-making.
Performs basic data analysis and support tasks in common data environments used by supported applications, including routine validation, access checks, reconciliation, and coordination with technical teams.
Participates in data testing approaches and test planning and assists with test procedures that assess the impact of data conversion, system upgrades, or process changes on data accuracy and reliability.
Works with OIT staff, functional offices, data stewards, vendors, and implementation partners to support data quality work on behalf of end users.
Verifies data extracts, validation outputs, reconciliation results, and assists with creating and maintaining technical documentation.
Monitors data quality reports, logs, dashboards, or issue lists and investigates discovered issues or events.
Ensures data handling and validation activities meet university security standards and requirements.
Provides data quality, reporting, or validation guidance to end users as needed.
Serves as a backup for peer staff supporting identified data sets, reports, processes, systems, or applications.
Maintains a positive, service-oriented approach when working with faculty, staff, and students.
Supports data quality and ERP migration projects by completing assigned validation, reconciliation, documentation, and issue-tracking tasks.
Other duties as assigned.

Data Quality Engineer II:
All the duties of Data Quality Engineer I, plus:
Assists in establishing data quality rules, validation criteria, reconciliation procedures, and source-to-target mapping documentation.
Contributes to data conversion control and release management procedures, maintains data validation documentation, and supports successful conversion or production deployments.
Helps establish data access practices, monitoring and control procedures, and data recovery or reconciliation procedures, including related testing.
Assists with security or privacy of incident investigations related to institutional data or supported systems.
Works with the Information Security team to mitigate risks related to institutional data and core OIT systems.
Establishes repeatable data validation checks and participates in data readiness, data volume, and conversion planning.
Supports data quality activities for one or more low-complexity data domains, reports, applications, or systems with guidance from more experienced staff.

Data Quality Engineer III:
All the duties of Data Quality Engineer II, plus:
Develops data quality strategies for related systems and domains, including validation approaches, remediation practices, improvement priorities, and coordination with stakeholders and technical partners.
Develops data conversion control and release management procedures, maintains data validation documentation, and helps ensure successful conversion or production deployments.
Uses scripting, automation, query development, or other technical tools to support data profiling, validation, cleansing, reconciliation, monitoring, reporting, and repeatable operations.
Supports integrations and data exchange across systems, including coordination with technical partners, data-flow validation, reconciliation, and integration troubleshooting.
Onboards new or existing data sources, reports, conversion processes, or software solutions using organizational and industry best practices for implementation and data management.

Senior Data Quality Engineer:
All the duties of Data Quality Engineer III, plus:
Serves as an expert resource for complex data quality analysis, reconciliation, conversion, remediation, and troubleshooting.
Leads data quality activities for one or more high-complexity data domains, applications, systems, or services.
Leads technical data quality projects with broad organizational impact.
Leads or significantly contribute to design, scalability, resiliency, and long-term support planning for complex enterprise data quality solutions, validation frameworks, and reconciliation processes.
Leads communication, coordination, and stakeholder engagement for complex data quality work, ensuring stakeholders and technical partners understand impacts, decisions, timelines, risks, data readiness, and support expectations.
Evaluates new data quality tools, data management approaches, software solutions, and works with business offices to address business needs or improve processes.
Supervision Exercised
None.
Message to Applicants
Central Michigan University is dedicated to fostering an environment that is reflective of the communities we serve. We are especially interested in highly qualified candidates who will advance and promote CMU's mission, vision, and leadership standards., CMU does not discriminate on the basis of sex in the education program or activity that it operates, including admission and employment, and is required by Title IX of the Education Amendments of 1972 not to discriminate in such a matter.
Inquiries about the application of Title IX can be made to CMU's Title IX Coordinator, the US Department of Education's Assistant Secretary, or both.
CMU's Title IX Coordinator can be reached at