Data Quality Lead

Job Description

The Data Quality Lead 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.

• 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 Data Ops, 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

• 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.

• 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