Data Quality Engineer

Position Summary
The Data Quality Engineer owns the implementation and continuous improvement of data
quality across critical business domains. The role ensures data is accurate consistent and usable
across enterprise systems including ERP CRM analytics platforms and customer-facing
applications while reducing operational inefficiencies and improving trust in data.
Key Responsibilities
Define implement and maintain data quality rules aligned to business processes and use
cases.
Monitor data quality across core ISO-8000 data quality standards (i.e. completeness
accuracy consistency validity uniqueness referential integrity and timeliness).
Design and implement data quality monitoring and alerting to proactively identify issues
as they arise.
Perform root cause analysis on data issues across source systems pipelines and
downstream applications.
Own cross-system data quality validation ensuring data integrity is enforced at ingestion
transformation and load stages
Partner with business stakeholders to define data ownership business definitions and
quality expectations.
Design and support remediation workflows to ensure timely resolution of data quality
issues with clear ownership and resolution SLAs.
Support implementation of data classification standards (e.g. PII sensitive data) in
alignment with GDPR CCPA applicable regulatory requirements and AI initiatives.
Leverage Microsoft Purview to improve visibility into data lineage definitions
ownership and classification.
Define and track data quality KPIs; report regularly on quality trends issue resolution
rates and business impact to leadership and governance stakeholders
Required Qualifications
Bachelor’s Degree in Information Technology Computer Science Data Analytics or a
related field.
Minimum of 3-5 years of experience in data quality data engineering data governance
or analytics roles.
Strong proficiency in SQL for data analysis and validation.
Experience working with data pipelines and transformation tools (Azure Data Factory
preferred).
Strong analytical and problem-solving skills with the ability to diagnose data issues
across systems.
Strong communication skills with the ability to work effectively with both technical and
non-technical stakeholders.
Ability to translate business needs into practical data quality solutions.
Ability to operate independently with limited day-to-day supervision in a distributed team
environment.
Solid understanding of relational data modeling concepts including entity relationships
normalization and schema design across systems.
Preferred Qualifications
Experience working in commercial distribution and/or light manufacturing
environments.
Experience with data governance and metadata management tools (e.g. Microsoft
Purview Collibra Alation Informatica)
Familiarity with master data management concepts (Customer and Product domains).
Experience implementing data quality frameworks or working with standards such as
DAMA-DMBOK or ISO-8000.
Exposure to CRM (Salesforce HubSpot) and/or ERP (Epicor).
Experience supporting analytics platforms such as Power BI.
Key Competencies
Data-driven problem solving in an enterprise environment.
Cross-functional collaboration.
Stakeholder engagement and influence.
Process improvement and operational efficiency.