About the RoleWe are looking for a Senior Data Quality Engineer to drive enterprise-wide data quality and ensure trusted, accurate, and reliable data for analytics and reporting with a leading Government entity in Dubai.

The ideal candidate will have strong hands-on experience in Data Quality Engineering, Data Profiling, SQL, Python/PySpark, Data Reconciliation, Automated Data Quality Controls, Cloud Data Platforms, Lakehouse/Medallion Architecture, Metadata Management, and Data Governance.

• Key ResponsibilitiesDefine and maintain Enterprise Data Quality Frameworks, standards, KPIs, thresholds, dimensions, and controls.
• Conduct data profiling, data-quality assessments, and source-data analysis.
• Design and implement automated data-quality checks across ingestion, transformation, curated, and analytical layers.
• Develop validation, rejection/quarantine, exception-handling, and remediation rules within data pipelines.
• Build source-to-target reconciliation controls and monitor data completeness, accuracy, and consistency.
• Monitor DQ dimensions including accuracy, completeness, validity, consistency, uniqueness, timeliness, and referential integrity.
• Investigate data-quality issues, conduct root-cause analysis, and drive remediation with Data Engineers, Data Owners, and technical teams.
• Translate business rules, keys, reference data, constraints, and historical requirements into technical data-quality controls.
• Support Microsoft Purview and MDM platforms such as Profisee, including Critical Data Elements (CDEs), duplicate detection, master/reference data validation, and golden-record quality.
• Maintain traceability between DQ rules, business requirements, metadata, data models, and source-to-target mappings.

• Required Skills & Experience4+ years of experience in Data Quality, Data Engineering, Data Management, or Data Governance.
• Strong hands-on experience in Data Profiling, Data Quality Rules, Data Validation, Reconciliation, Monitoring, and Remediation.
• Advanced SQL skills with practical experience in Python, PySpark, Spark, or equivalent technologies.
• Strong understanding of relational and dimensional data modelling, business/surrogate keys, normalization, referential integrity, and Slowly Changing Dimensions (SCD).
• Experience with Cloud Data Platforms and Lakehouse/Medallion Architecture.
• Strong analytical, problem-solving, documentation, communication, and stakeholder-management skills.