Data Quality Lead (m/f/d)

Halian | Managed Services, Recruitment Agency & Contract Staffing; Abu Dhabi; On-site; AED 250,000 - 460,000; Full time

Job summary

Halian | Managed Services, Recruitment Agency & Contract Staffing seeks a Data Quality Lead to own end-to-end QA and delivery assurance for data quality, readiness, and modelling deliverables before client submission. The role acts as the final internal quality gate across data artefacts and governance processes.

The ideal candidate has strong expertise in Data Quality, Data Modelling, Data Governance, and QA, with the ability to independently review complex deliverables and identify

Qualifications

• Strong experience in data quality and governance.
• Hands-on understanding of Conceptual, Logical, and Physical data models.
• Proficient in SQL for data validation and traceability.
• Ability to review complex deliverables and identify cross-functional inconsistencies.
• Experience in consulting or client-facing environments preferred.

Responsibilities

• QA client submission packages including data profiling and CDEs.
• QA data modelling deliverables across conceptual, logical, physical models.
• Validate model components: entities, keys, relationships, and naming conventions.
• Reconcile cross-deliverables for scope, rules, and DQ scores.
• Maintain QA checklist and issue register, assign severity.

Skills

Data Quality Assurance

Data Governance

Data Modelling

SQL Validation

Stakeholder Management

Analytical Thinking

Documentation

Independence

Job description

• Model documentation
• CDEs
• DQ scores

Role Purpose

Own the independent, end-to-end Quality Assurance (QA) and delivery assurance of Data Quality, Data Readiness, and Data Modelling deliverables before submission to the client.

The role will act as the final internal quality gate, ensuring that all deliverables are accurate, complete, consistent, traceable, and aligned with the agreed project methodology, architecture, dependencies, business requirements, and client acceptance criteria.

The ideal candidate should have strong experience in Data Quality, Data Modelling, Data Governance, and QA, with the ability to independently review complex deliverables and identify cross-functional inconsistencies before client submission.

Key Responsibilities

• Independently QA the complete client submission package, including:
  • Data profiling outputs
  • Critical Data Element (CDE) scope
  • Data Quality rulebooks
  • Bronze and Silver execution logs
  • Failed records
  • Data Quality scores
  • Dashboards and reports
  • Annexes and supporting documentation
  • Remediation trackers
  • Closure reports and supporting evidence
• Perform detailed QA of data modelling deliverables, including conceptual, logical, and physical data models.
• Validate data model components such as:
  • Entities and attributes
  • Relationships and cardinalities
  • Primary and foreign keys
  • Naming conventions and standards
  • Data types
  • Model documentation
• Validate alignment between data models and downstream Data Quality deliverables, ensuring consistency across tables, entities, attributes, CDEs, keys, and business definitions.
• Perform cross-deliverable reconciliation to identify inconsistencies or mismatches across:
  • Scope
  • Rule IDs
  • CDEs
  • Data Quality dimensions
  • Rule counts
  • Execution results
  • Failed records
  • DQ scores
  • Timestamps
  • Remediation status
  • Dashboard reporting
• Independently reproduce or validate critical Data Quality calculations and ensure that reported Bronze and Silver scores are fully traceable to the underlying execution evidence and agreed methodology.
• Validate completeness of deliverables against agreed client requirements, project scope, and acceptance criteria, including coverage of tables, CDEs, models, DQ dimensions, rules, evidence, and required documentation.
• Review exclusions, exceptions, low-coverage areas, and source-system limitations, ensuring that appropriate rationale, impact assessment, and supporting evidence are documented.
• Maintain a structured QA checklist and issue register, ensuring all findings are properly documented and tracked through closure.
• Classify QA findings based on severity, business impact, and client submission risk.
• Work closely with Data Quality, Data Modelling, Data Governance, and other workstream teams to drive timely resolution of identified issues.
• Conduct final readiness reviews and provide a clear “Ready / Not Ready for Client Submission” recommendation.

Key Skills & Experience

• Strong experience in Data Quality Assurance, Data Governance, Data Modelling, or Data QA.
• Hands-on understanding of Conceptual, Logical, and Physical Data Models.
• Strong knowledge of data profiling, CDEs, DQ dimensions, DQ rules, scoring, and remediation processes.
• Experience performing cross-functional and cross-deliverable reconciliation.
• Strong SQL skills for data validation and independent verification of DQ results.
• Ability to trace reported metrics and scores back to underlying execution evidence.
• Strong understanding of relational databases, primary/foreign keys, relationships, cardinality, and data structures.
• Experience working with large and complex data transformation or data quality projects.
• Strong attention to detail with an ability to identify inconsistencies across multiple deliverables.
• Excellent analytical, problem-solving, documentation, and stakeholder management skills.
• Ability to work independently and confidently challenge deliverables before client submission.
• Experience working in consulting or client-facing environments is preferred.

Key Competencies

• Data Quality & Data Governance
• Data Modelling
• QA & Delivery Assurance
• SQL & Data Validation
• Cross-Deliverable Reconciliation
• Root Cause Analysis
• Issue & Risk Management
• Attention to Detail
• Stakeholder Management
• Client Delivery Readiness

Role Outcome

The successful candidate will ensure that only complete, accurate, consistent, evidence-backed, and client-ready deliverables are submitted, minimizing quality issues, inconsistencies, and rework during client review.

Data Quality Lead in Abu Dhabi, United Arab Emirates