Data Quality Consultant

Role Overview

The Data Quality Consultant collaborates with clients to evaluate, quantify, and enhance the suitability of data for operational, analytical, and public service purposes. This position involves data profiling, defining quality criteria, establishing thresholds and ongoing monitoring, investigating root causes of issues, and supporting sustainable remediation and issue management.

The consultant partners with Data Owners, Data Stewards, business stakeholders, system architects, engineers, and technical teams to ensure that quality targets align with actual business requirements and trusted reference sources. While the consultant provides detailed findings, recommendations, and corrective action plans, ultimate responsibility for risk acceptance and approval lies with client management. Success in this role demands a balance of analytical skills and facilitation capabilities to translate technical insights into actionable ownership, improved processes, and control enhancements.

Key Responsibilities

• Identify essential datasets, key attributes, and business processes for data quality oversight.
• Profile data to detect issues with completeness, validity, consistency, uniqueness, timeliness, accuracy, and accessibility.
• Collaborate with business partners to define data quality rules, thresholds, reference data sources, calculation methods, and exception cases.
• Create quality assessment frameworks including rules registries, scorecards, monitoring specifications, and evidence protocols.
• Compute and verify quality metrics, communicating their significance for both operational and analytical applications.
• Maintain registers of data quality issues and assist in prioritizing them transparently based on severity, urgency, frequency, sensitivity, and complexity.
• Lead root-cause investigations considering process workflows, personnel, systems, integrations, and data architecture.
• Develop and manage remediation plans with clear owners, deadlines, dependencies, validation stages, and closure documentation.
• Coordinate quality control measures with metadata, master data management, architecture, engineering, security, and analytics divisions.
• Design dashboards and routine reports to support continuous monitoring and escalation procedures.
• Validate remediation effectiveness distinguishing fixed records from resolved root causes.
• Provide training to client teams on rule maintenance, metric interpretation, issue diagnosis, and exception governance.

Qualifications and Experience

• Bachelor’s degree in data science, statistics, computer science, information systems, or related field; equivalent combinations of education and relevant direct consulting or implementation experience considered.
• 4 to 8 years of professional experience in data quality, data analysis, governance, testing, or data engineering roles.
• Hands-on proficiency profiling datasets and establishing or validating quality rules and thresholds.
• Experience in root cause analysis, remediation support, and data quality monitoring.
• Skilled at communicating quantitative findings clearly to technical and business audiences.

Technical Knowledge and Competencies

• Mastery of data quality dimensions, rule creation, threshold setting, and measurement methodologies.
• Expertise in data profiling, anomaly detection, and reference data comparison techniques.
• Adept at prioritizing data issues, conducting root-cause analyses, and tracking remediation.
• Proficient in developing quality scorecards, dashboards, monitoring solutions, and escalation mechanisms.
• Strong SQL skills and analytical methods for data validation.
• Understanding of metadata, data lineage, master data management, and architectural dependencies.
• Competence documenting business rules and supporting control evidence.
• Appreciation of quality requirements for operational, analytical, and shared data contexts.

Core Skills and Attributes

• Data profiling expertise.
• Rule design capabilities.
• Root-cause analytical thinking.
• Advanced SQL and data analysis techniques.
• Effective issue prioritization strategies.
• Experience in quality monitoring and continuous improvement.
• Strong facilitation skills to lead rule definition and root-cause workshops.
• Ability to communicate complex calculations, evidence bases, and limitations transparently.
• Precision in writing quality rules, findings, and remediation plans.
• Collaborative approach working with data owners, stewards, and technical teams.
• Willingness to challenge unsupported data fitness assumptions.
• Focus on business impact when prioritizing issues rather than volume alone.
• Capability to enable clients to sustain monitoring and issue investigation independently.

Preferred Certifications

• CDMP Associate or Practitioner, ideally with a Data Quality specialty.
• ISTQB CTFL certification, especially if testing plays a significant role.