Data Quality Lead

Strong working knowledge of data quality management principles, frameworks, and industry practices
Deep understanding of data quality dimensions such as completeness, validity, consistency, uniqueness, integrity, accuracy, timeliness, and conformity
Hands-on experience with data profiling tools, data quality tools, SQL-based analysis, or equivalent data assessment methods
Strong ability to interpret business rules and translate them into measurable data rules, checks, thresholds, and expected results
Working knowledge of data validation, reconciliation, exception analysis, sampling, and evidence-based issue investigation
Practical experience with data quality dashboards, scorecards, metrics, and performance reporting
Strong SQL literacy and ability to review analytical outputs produced by analysts
Good understanding of data pipelines, source systems, integration patterns, reporting layers, and data lifecycle impacts on quality
Working knowledge of data governance, metadata, data ownership, data stewardship, and data issue management concepts
Ability to distinguish between data defects, process defects, system defects, integration defects, definition problems, and usage problems
Ability to communicate technical data quality findings in clear business language
Ability to lead workshops and discussions with business, data, technology, risk, and operations teams
Ability to coach analysts and review their work for quality, completeness, logic, and evidence
Strong documentation skills for data quality methods, rules, standards, dashboards, and improvement recommendations

Lead the organization’s data quality approach across measurement, assessment, improvement, and monitoring
Define data quality dimensions, measurement methods, scoring approaches, and quality thresholds
Lead data profiling and data quality assessment activities
Work with business stakeholders to interpret data issues in terms of operational, reporting, compliance, customer, or financial impact
Define and validate data quality rules, data checks, expectations, and acceptance criteria
Guide investigation of source data, business rules, process behavior, transformation logic, and reporting outputs
Define approaches for data quality controls, monitoring, exception management, issue management, and improvement tracking
Support remediation planning by clarifying issue patterns, treatment options, business ownership, and expected outcomes
Define data quality KPIs, reporting views, dashboards, and scorecards
Ensure alignment between data quality, data governance, metadata, privacy, security, reporting, analytics, and AI requirements
Review analysis performed by Senior Data Quality Analysts and Data Quality Analysts
Provide functional guidance to DataOps, engineering, platform, and reporting teams
Support business validation and acceptance of data quality outputs
Promote sustainable data quality practices that can be embedded into business-as-usual operations
Support knowledge transfer, capability uplift, and continuous improvement

The Data Quality Lead is not only a technical analysis role. It is a senior role that combines data quality expertise, business interpretation, analytical judgment, governance awareness, and stakeholder leadership. The role should help organizations move from reactive data fixing to measurable, controlled, and sustainable data quality management.

Bachelor’s degree in Information Systems, Computer Science, Statistics, Mathematics, Business Analytics, Economics, Engineering, Management Information Systems, or a related discipline.

Master’s degree in Analytics, Information Systems, Business Administration, Statistics, Digital Transformation, or a related discipline is desirable.

6–10+ years of experience in data quality, data analysis, data profiling, data governance, master data management, data migration, data integration, reporting, analytics, or enterprise data improvement programs.

Experience leading data quality assessments, defining data quality measurement approaches, working with business rules, designing data quality controls, managing data issue processes, and working across business and technical teams is preferred.

Preferred certifications include:

DAMA CDMP Practitioner or Master
DAMA CDMP Associate for candidates progressing toward senior certification
Informatica Data Quality / IDMC certification where Informatica is relevant
Microsoft, AWS, Google Cloud, Snowflake, Databricks, or equivalent data platform certification where relevant
SQL, analytics, BI, or data engineering certification where relevant
Six Sigma Green Belt or Lean certification where process improvement is relevant