Data Quality Lead (m/f/d)

Role Purpose

Key Responsibilities

• QA client submission packages covering profiling, CDE scope, rulebooks, execution logs, failed records, scores, dashboards, annexes, trackers, and closure evidence.
• Review conceptual, logical, and physical data models and validate entities, attributes, relationships, keys, standards, data types, and documentation.
• Reconcile models and Data Quality deliverables across scope, rules, CDEs, dimensions, scores, evidence, remediation, and reporting.
• Validate calculations, completeness, exclusions, exceptions, coverage limitations, rationale, impact assessments, and supporting evidence.
• Maintain QA checklists and issue registers, classify findings, and work with workstream teams through resolution.
• Conduct final readiness reviews and provide a Ready or Not Ready for Client Submission recommendation.

Key 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 profiling, CDEs, data quality dimensions, rules, scoring, and remediation.
• Experience with cross-functional and cross-deliverable reconciliation and complex data quality projects.
• Strong SQL skills for validation and independent verification of data quality results.
• Ability to trace reported metrics to execution evidence and understand relational database structures.
• Strong analytical, problem-solving, documentation, attention-to-detail, and stakeholder-management skills.
• Ability to work independently and challenge deliverables before client submission.
• Consulting or client-facing experience is preferred.

Core 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.
• Client Delivery Readiness.

Role Outcome