Data Quality Analyst

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