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Data Modelling Lead (m/f/d)
Halian | Managed Services, Recruitment Agency & Contract Staffing
Abu Dhabi, UAE
Full-time
Mid-Senior
Onsite
Discovered 2 days ago
Data qualityData governanceData readiness assessmentConceptual data modellingLogical data modellingPhysical data modelling
Free
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Role Overview
Act as the senior technical subject-matter expert and trusted advisor to project leadership across data quality, data readiness, and data modelling.
Shape technical positions, challenge project outputs, resolve complex data issues, and support client discussions, workshops, and decision points.
Translate complex data challenges into clear and defensible recommendations for senior stakeholders and clients.
Key Skills for This Role
Data qualityData governanceData readiness assessmentConceptual data modellingLogical data modellingPhysical data modelling
Full Job Posting
Role Purpose
Act as the senior technical subject-matter expert and trusted advisor to project leadership across data quality, data readiness, and data modelling.
Shape technical positions, challenge project outputs, resolve complex data issues, and support client discussions, workshops, and decision points.
Translate complex data challenges into clear and defensible recommendations for senior stakeholders and clients.
Key Responsibilities
- Advise project leadership on data quality, data readiness, and data modelling topics before key client engagements and submissions.
- Provide guidance on data profiling, Critical Data Elements, data quality dimensions, rule design, thresholds, scoring, exceptions, exclusions, root cause analysis, and remediation.
- Review conceptual, logical, and physical data models, including design, relationships, keys, normalization, data types, naming conventions, and business alignment.
- Assess consistency across models, source systems, Critical Data Elements, data quality rules, and target data architecture.
- Determine appropriate treatment for complex issues, including remediation, exceptions, model changes, scope adjustments, or methodology changes.
- Challenge methodologies, assumptions, technical approaches, and conclusions before client presentation.
- Connect data quality and modelling outputs to provide a holistic view of data readiness.
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