Client's analytics, reporting and AI ambitions depend on data that decision-makers can trust. The Senior Data Quality Engineer is accountable for making that trust measurable. They own the enterprise Data Quality (DQ) framework and embed automated controls at every layer of the lakehouse, from ingestion through to curated and analytical data. They also ensure that every quality issue is traced, owned and resolved at its root cause.
The role works alongside Client's Data Owners, Data Stewards and Sutherland's data engineering team. Its job is to turn business rules into enforceable technical controls, using SUTHERLAND's Microsoft Purview investment as the governance and master data backbone.
Key Responsibilities on SUTHERLAND Engagement
- Enterprise DQ Framework: Define, operationalize and maintain Client's DQ framework, including standards, dimensions, KPIs, thresholds and controls, aligned to ISO 8000 and DAMA-DMBOK2 and to Dubai Data Law data quality expectations.
- Profiling and baseline assessment: Conduct source-data profiling and DQ assessments during Mobilization & Discovery to establish the quality baseline that all improvement KPIs are measured against.
- Automated controls across the medallion architecture: Design and implement DQ checks at the Bronze (ingestion), Silver (transformation/conformance), Gold (curated) and analytical layers.
- Pipeline-embedded validation: Build validation, rejection/quarantine, exception-handling and remediation rules directly into data pipelines so that poor-quality data never reaches reporting consumers unflagged.
- Reconciliation assurance: Establish source-to-target reconciliation controls that evidence completeness, accuracy and consistency for every critical data flow.
- Continuous DQ monitoring: Monitor accuracy, completeness, validity, consistency, uniqueness, timeliness and referential integrity through dashboards visible to SUTHERLAND data owners and the Data Governance Council.
- Root-cause analysis and remediation: Lead investigation of DQ incidents and drive remediation with data engineers, Data Owners and source-system teams through a governed issue workflow.
- Business-rule translation: Convert business rules, keys, reference data, constraints and historical requirements into executable technical DQ controls.
- Purview and MDM enablement: Support Microsoft Purview and the MDM platform, including Critical Data Element (CDE) identification, DQ scoring, duplicate detection, master/reference data validation and golden-record quality assurance.
- End-to-end traceability: Maintain traceability between DQ rules, business requirements, metadata, data models and source-to-target mappings, so that every control is auditable.
- Knowledge transfer: Coach Client's counterparts and data stewards in DQ practices and tooling, so that data quality can be sustained independently after handover.
Qualifications and Experience
- 4+ years in Data Quality, Data Engineering, Data Management or Data Governance, preferably within UAE government or regulated-sector environments.
- Proven hands‑on delivery of data profiling, DQ rules, validation, reconciliation, monitoring and remediation.
- Advanced SQL, with practical Python, PySpark or Spark experience.
- Strong grounding in relational and dimensional modelling, including business and surrogate keys, normalisations, referential integrity and Slowly Changing Dimensions (SCD).
- Experience with cloud data platforms and Lakehouse/Medallion architecture (Azure / Microsoft Fabric preferred).
- Working knowledge of Microsoft Purview and MDM platforms.
- Familiarity with UAE data regulation, including UAE PDPL, the Dubai Data Law and DESC ISR.
- Excellent English communication, documentation and stakeholder‑management skills; Arabic proficiency is an advantage.