We’re partnering with a well-established Australian organisation currently undertaking a significant technology and data transformation program.

They’re looking for a mid-level Data Quality Analyst to join the program and take a hands-on role in identifying, investigating and remediating data quality issues across a large and complex data environment.

This is a technical Data Quality role rather than a purely governance-focused position. You’ll be working directly with Microsoft SQL Server and T-SQL, profiling data, translating business rules into data quality checks and developing remediation scripts to correct identified issues.

What you’ll be doing

• Profile large datasets and identify data quality issues across completeness, accuracy, consistency, validity, uniqueness and timeliness.
• Translate business rules into testable data quality rules and T-SQL validation queries.
• Investigate anomalies and perform root-cause analysis.
• Write, test and execute SQL/T-SQL remediation scripts to correct data issues.
• Validate data following remediation to ensure issues have been resolved without introducing new problems.
• Produce Data Quality metrics, scorecards and reporting.
• Work with business and technical SMEs to determine appropriate treatment of data issues.
• Maintain clear documentation of DQ rules, assessment logic and remediation activities.

The role covers the full data-quality lifecycle from assessment and profiling through to treatment, remediation and post-treatment validation.

Essential experience To be considered, you must have:

• Strong hands-on Microsoft SQL Server and T-SQL experience, including writing complex queries and scripts.
• Practical Data Quality experience — not simply reporting, analytics or data governance.
• Experience with data profiling, root-cause analysis and data remediation.
• Strong understanding of core Data Quality dimensions and methodologies.
• Experience working with large datasets and optimising SQL queries for performance.
• Understanding of data governance, DQ metrics/scorecards and issue-management processes.
• Strong documentation and stakeholder communication skills.
• These requirements come directly from the essential criteria in the position d

We’re partnering with a well-established Australian organisation currently undertaking a significant technology and data transformation program.

They’re looking for a mid-level Data Quality Analyst to join the program and take a hands-on role in identifying, investigating and remediating data quality issues across a large and complex data environment.

This is a technical Data Quality role rather than a purely governance-focused position. You’ll be working directly with Microsoft SQL Server and T-SQL, profiling data, translating business rules into data quality checks and developing remediation scripts to correct identified issues.

What you’ll be doing

• Profile large datasets and identify data quality issues across completeness, accuracy, consistency, validity, uniqueness and timeliness.
• Translate business rules into testable data quality rules and T-SQL validation queries.
• Investigate anomalies and perform root-cause analysis.
• Write, test and execute SQL/T-SQL remediation scripts to correct data issues.
• Validate data following remediation to ensure issues have been resolved without introducing new problems.
• Produce Data Quality metrics, scorecards and reporting.
• Work with business and technical SMEs to determine appropriate treatment of data issues.
• Maintain clear documentation of DQ rules, assessment logic and remediation activities.

The role covers the full data-quality lifecycle from assessment and profiling through to treatment, remediation and post-treatment validation.

Essential experience To be considered, you must have:

• Strong hands-on Microsoft SQL Server and T-SQL experience, including writing complex queries and scripts.
• Practical Data Quality experience — not simply reporting, analytics or data governance.
• Experience with data profiling, root-cause analysis and data remediation.
• Strong understanding of core Data Quality dimensions and methodologies.
• Experience working with large datasets and optimising SQL queries for performance.
• Understanding of data governance, DQ metrics/scorecards and issue-management processes.
• Strong documentation and stakeholder communication skills.
• These requirements come directly from the essential criteria in the position d