Title: Data Quality Analyst
Location: Charlotte, NC
(4 Days Onsite in office, 1 days remote)
Duration: 12+ months, possibility to extend or convert
Job Objective:
Proactive data-quality checking + data profiling + data-issue identification/management before data reaches AML models.
The project is centered around AML data models and improving the quality of the data feeding those models.
The objective is to identify and resolve data problems before they reach the model, rather than discovering them after the model has consumed the data.
Key Responsibilities and Required Skills
Hands-on SQL Development: Write and execute SQL queries to extract, analyze, and validate data from the Teradata environment.
Data Quality Rule Definition: Define, develop, and implement data quality rules to assess data accuracy, completeness, consistency, and integrity.
Data Profiling & Analysis: Perform data profiling using SQL to understand data structures, identify patterns, detect anomalies, and assess data quality.
Data Validation & Issue Identification: Identify data discrepancies, investigate data quality issues, and support root cause analysis and remediation.
Data Requirements & Documentation: Document data quality rules, profiling results, validation logic, and findings to support consistent data management practices
Project deliverables
- Proactive data-quality checks: Establish checks that identify problematic data before it enters or impacts AML models.
- Data profiling: Profile datasets to understand data characteristics, quality issues, anomalies, and potential problems.
- Data issue / defect identification: Detect data defects or quality issues affecting the datasets used by AML models.
- Data issue management and resolution: Track, investigate, and help remediate identified data-quality issues rather than simply reporting them.
- Model-input quality assurance: Provide confidence that the data being supplied to AML models meets expected quality standards before it is consumed by the models.
- Preventive controls / monitoring: Move the process from reactive detection to preventive monitoring so issues are caught earlier in the data lifecycle.
• Demonstrated accountability for analytical or quantitative models across implementation, maintenance, and controlled change processes.
• Experience operating within governed environments requiring formal documentation, controls, and audit readiness.
• Ability to independently challenge model assumptions and interpret outputs without serving as primary model developer.
• Proficient in performing data analysis and validation using SQL in Teradata environments.
• Ability to analyze large datasets to identify trends, issues, and insights supporting business decisions.
• Experience monitoring, validating, and reporting on data quality metrics including accuracy, completeness, and timeliness.
• Strong capability to conduct root cause analysis for data quality issues.
• Demonstrated experience partnering with stakeholders to drive resolution of data defects and control gaps.
• Proven experience managing complex projects with multiple stakeholders across technology, data, and business teams.
• Ability to coordinate timelines, deliverables, dependencies, and risk issues to successful completion.
EEO:
“Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of – Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.”
