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.

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