Data Quality Engineer

Data Quality Engineer

Location: Charlotte, NC - Hybrid (2 days onsite)

Job Summary

STAFFXPERT LLC is seeking a Data Quality Engineer on behalf of our client in Charlotte, NC to support enterprise data governance, data quality, and data lineage initiatives. This role will focus on implementing scalable data governance and quality solutions using Collibra and Azure-based data platforms , while partnering with Data Strategy, Business, and Data Engineering teams to deliver trusted, governed data.

Key Responsibilities

• Develop and maintain data quality rules using Collibra DQ.

• Implement and manage data governance assets , including business glossaries, data catalogs, and stewardship workflows.

• Support data lineage mapping across systems and data pipelines.

• Perform data profiling, validation, and issue analysis using SQL.

• Integrate Collibra with Azure data platforms, including ADF, Databricks, and Data Lake .

• Develop automation, utilities, and data quality solutions using Python or similar languages .

• Collaborate with Data Owners and Data Engineers to define and enforce data quality controls.

• Support CI/CD, Git, version control, and deployment best practices .

• Maintain technical documentation, governance artifacts, and project materials in tools such as Confluence and code repositories.

Required Qualifications

• 10+ years of experience in data engineering, data quality, data governance, or a related field.

• Strong experience with data quality, data governance, metadata management, and data lineage .

• Hands-on experience with Collibra , including data quality and governance capabilities.

• Strong SQL skills for data profiling, validation, and analysis.

• Experience with Azure data platforms , including ADF, Databricks, and Data Lake.

• Proficiency in Python or similar programming languages.

• Experience with Git, CI/CD, and deployment practices .

• Strong analytical, communication, and cross-functional collaboration skills.

Preferred Qualifications

• Experience in the financial services industry .

• Knowledge of financial instruments, asset classes, reference data, market data, and operational data .

• Experience supporting enterprise-scale digital transformation and data modernization initiatives.

• Familiarity with Confluence and enterprise documentation/governance practices.