Job Specifications
Data Quality Analyst
Role Objective
The Data Quality Analyst is responsible for safeguarding the reliability and usability of organisational data by ensuring it is accurate, complete, consistent, and appropriate for its intended use. The role requires strong analytical capability and investigative mindset to examine data issues, follow data flows end to end, and drive improvements both at source and across business processes. Acting at the intersection of business knowledge and technical delivery, the analyst promotes robust data standards and integrity.
Core Responsibilities
Data Analysis & Quality Evaluation
Conduct in-depth analysis of datasets to assess quality measures including accuracy, completeness, consistency, validity, and timeliness
Detect, record, and analyse recurring data quality defects, linking them to business impact and underlying causes
Design and maintain reporting or dashboards to monitor data quality performance and trends
Quality Rules & Controls
Work closely with business and technical teams to establish and enhance data quality rules
Embed automated quality checks within ETL pipelines, data platforms, or reporting solutions
Verify transformed data to ensure it conforms to agreed business definitions and requirements
Issue Investigation & Resolution
Analyse data exceptions to identify root causes at system or process level
Coordinate with data owners and system teams to introduce corrective and preventative actions
Propose long-term improvements to minimise recurring data quality risks
Data Governance Support
Contribute to data governance activities such as data dictionaries, quality standards, and stewardship practices
Encourage consistent interpretation and use of data across teams
Supply metrics, evidence, and analysis to support governance forums and data quality KPIs
Collaboration & Enhancement
Work alongside data engineers, migration specialists, business analysts, and subject matter experts to integrate quality controls into data flows
Identify opportunities to automate validation, cleansing, and exception handling
Help develop reusable frameworks, templates, and patterns for data quality management
Skills & Experience
Proven experience delivering data quality improvement initiatives across varied sectors; exposure to regulated environments is advantageous
Strong knowledge of data profiling, assessment methodologies, and quality dimensions
Hands-on SQL experience for data investigation, validation, and analysis
Practical understanding of remediation strategies and issue resolution approaches
Familiarity with data governance and quality tooling (e.g., Azure Purview or similar)
Experience defining data quality metrics, thresholds, and performance dashboards
Understanding of ETL processes and the impact of upstream transformations on data quality
Ability to translate business rules into clear, testable technical validations
Strong written and verbal communication skills, with the ability to present complex findings clearly and actionably
Personal Qualities
Curious and analytical, with a strong desire to understand data behaviour and anomalies
Detail-oriented and structured, while remaining pragmatic and outcome-focused
Confident in questioning assumptions and supporting conclusions with evidence
Views data quality as a foundational element of system design and user confidence
Collaborative by nature, with a commitment to continuous learning in dynamic data environments