Project Manager - Data Governance
Job in
Manama, Bahrain
Listed on 2026-08-09
Listing for:
Virtuthinko W. L
Full Time
position Listed on 2026-08-09
Job specializations:
-
IT/Tech
Data Engineering, Data Analyst, Information Security & Data Protection, Data Warehousing
Job Description & How to Apply Below
The Project Manager is responsible for leading the end-to-end delivery of assigned initiatives, ensuring alignment with the Group's business strategy, regulatory requirements, and digital goals.
Responsibilities- Centralized, searchable data catalog covering data assets across on prem, cloud, and hybrid environments
- Automated metadata discovery and harvesting
- Business and technical metadata support, including data definitions, ownership, and usage context
- Ability to document and manage data products and critical data elements
- End to end, preferably automated, data lineage (source to target)
- Visual lineage for business and technical users
- Impact analysis to assess downstream effects of changes to data, models, or systems
- Support for SQL, ETL, ELT, BI, and analytics pipelines
- Definition and monitoring of data quality rules and thresholds
- Support for profiling, validation, and exception management
- Data quality dashboards and scorecards
- Workflow for issue remediation and accountability
- Clearly defined governance roles (e.g., Data Owner, Data Steward, Custodian)
- Configurable workflows for approvals, certifications, and issue management
- Policy management and enforcement capabilities
- Evidence and audit trail for governance decisions
- Classification of sensitive and regulated data (e.g., PII, confidential data)
- Integration with enterprise Identity Access Management for role based access control
- Support for privacy regulations and internal data policies
- Data access transparency and auditability
- Support for AI ready data governance, including datasets, features, and models
- Trust, explainability, and lineage for AI and advanced analytics use cases
- Alignment with responsible and ethical AI principles
- Integration with analytics and machine learning platforms
- Native connectors or APIs for data platforms (cloud warehouses, lakes, ETL tools, BI, ML platforms)
- Open architecture to avoid vendor lock in
- Ability to integrate with existing enterprise architecture and tooling
- Proven capability to scale across large data estates and multiple domains
- Support for enterprise wide deployment and federated governance models
- High availability, reliability, and performance
- Intuitive user experience for both technical and business users
- Self service capabilities with appropriate controls
- Collaboration features (comments, annotations, ownership visibility)
- Governance KPIs and dashboards (e.g., data quality, adoption, compliance)
- Executive-level reporting capabilities
- Custom reporting and export options
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