Data Quality Lead (m/f/d)

Role Purpose

Deliverable and Model QA

• QA client submission packages covering profiling outputs, CDE scope, rulebooks, execution logs, failed records, scores, dashboards, reports, annexes, trackers, and closure evidence.
• Perform detailed QA of conceptual, logical, and physical data models.
• Validate entities, attributes, relationships, cardinalities, keys, naming conventions, data types, and model documentation.
• Validate consistency between data models and downstream data quality deliverables.

Reconciliation and Validation

• Reconcile scope, rule IDs, CDEs, data quality dimensions, rule counts, execution results, failed records, scores, timestamps, remediation status, and dashboard reporting.
• Reproduce or validate critical data quality calculations and trace reported scores to execution evidence and agreed methodology.
• Validate deliverable completeness against client requirements, project scope, and acceptance criteria.
• Review exclusions, exceptions, low-coverage areas, and source-system limitations and document rationale, impact, and evidence.

Issue Management and Readiness

• Maintain a structured QA checklist and issue register through closure.
• Classify findings by severity, business impact, and client submission risk.
• Work with data quality, data modelling, data governance, and other workstreams to resolve issues.
• Conduct final readiness reviews and recommend whether deliverables are ready for client submission.

Required Skills and 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, data quality dimensions, rules, scoring, and remediation.
• Experience with cross-functional and cross-deliverable reconciliation.
• Strong SQL skills for validation and independent verification of data quality results.
• Strong understanding of relational databases, keys, relationships, cardinality, and data structures.
• Experience with large and complex data transformation or data quality projects.
• Strong analytical, problem-solving, documentation, attention-to-detail, and stakeholder-management skills.
• Ability to work independently and challenge deliverables before client submission.

Preferred Experience

• Experience working in consulting or client-facing environments is preferred.

Key Competencies

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

Role Outcome