Role Purpose

The Data Quality Consultant helps clients understand, measure and improve the fitness of data for operational, analytical and public-service use. The role profiles data, defines quality dimensions and rules, establishes thresholds and monitoring, investigates root causes and supports sustainable remediation and issue-management processes.

The consultant works with Data Owners, Data Stewards, business experts, architects, engineers and system teams to ensure that quality expectations are tied to real business needs and authoritative reference sources. The role prepares evidence, recommendations and remediation plans, but client owners remain accountable for accepting quality risks and approving corrective actions. Effective delivery requires both analytical capability and facilitation skill so that technical findings become practical ownership, process and control improvements.

Key Responsibilities

Identify critical datasets, attributes, decisions and business processes requiring data-quality management. Profile data to identify completeness, validity, consistency, uniqueness, timeliness, accuracy and accessibility issues. Define data-quality rules, thresholds, reference sources, calculation logic and exception conditions with business stakeholders. Develop quality assessment plans, rule registers, scorecards, monitoring specifications and evidence requirements. Calculate and validate quality results and explain the implications for operational and analytical use. Maintain data-quality issue registers and support transparent prioritisation based on impact, urgency, frequency, sensitivity and complexity. Facilitate root-cause analysis across process, people, system, integration and data-design factors. Develop remediation plans with owners, target dates, dependencies, validation steps and closure evidence. Coordinate quality controls with metadata, master data, architecture, engineering, security and analytics teams. Define dashboards and reporting routines for ongoing monitoring and escalation. Validate remediation outcomes and distinguish corrected records from resolved root causes. Train client teams to maintain rules, interpret measures, investigate issues and govern exceptions.

Minimum Requirements

Education

Bachelor's degree in data science, statistics, computer science, information systems or a related field. An equivalent combination of relevant education and directly applicable consulting or implementation experience may be considered.

Professional Experience

Typically 4-8 years in data quality, data analysis, testing, governance or data engineering. Experience in data quality, data analysis, data governance, business intelligence or related data-management work. Practical experience profiling data and defining or testing quality rules and thresholds. Experience supporting root-cause analysis, remediation and quality monitoring. Experience explaining quantitative findings to business and technical stakeholders.

Technical and Domain Knowledge

Data-quality dimensions, rules, thresholds and measurement methods. Data profiling, anomaly detection and reference-data comparison. Issue prioritisation, root-cause analysis and remediation tracking. Quality scorecards, monitoring, dashboards and escalation. SQL and analytical techniques for validating data. Metadata, lineage, master data and architecture dependencies. Business-rule documentation and control evidence. Quality considerations for operational, analytical and shared data.

Core Competencies

Data profiling. Rule design. Root-cause analysis. SQL and analysis. Issue prioritization. Quality monitoring. Analytical rigour. Root-cause thinking. Evidence discipline. Continuous improvement. Facilitating rule-definition and root-cause workshops. Explaining calculations, evidence and limitations clearly. Writing precise rules, findings and remediation actions. Working constructively with data owners, stewards and technical teams. Challenging unsupported assumptions about data fitness. Prioritising issues based on business impact rather than volume alone. Transferring repeatable monitoring and investigation practices to clients.

Preferred Certifications

CDMP Associate or Practitioner, with Data Quality specialty preferred ISTQB CTFL where testing is material

Focus areas

Data profiling Rule design Root-cause analysis

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