Data Management Lead (Enterprise Data)
Absa
n/a - n/a
R800–850 per hour
Auckland Park, City of Johannesburg Metropolitan Municipality, 2092
Contract
Stakeholder Management
Data Governance
Data quality
Master Data Management
Metadata management
Role
Role Purpose
The Data Management Lead is responsible for designing, implementing, and operationalizing enterprise data management capabilities within Corporate & Investment Banking (CIB).
This role will drive data governance, data quality, and master/reference data management (MDM/RDM) across business domains, ensuring trusted, high-quality data aligned with regulatory requirements and business objectives.
The incumbent will define and lead target-state data architecture, authoritative data sourcing, and enterprise data flows, leveraging industry-standard data management tools and frameworks.
Key Responsibilities
1. Enterprise Data Management Delivery
- Lead the end-to-end delivery of enterprise data management initiatives across CIB.
- Drive adoption of data governance frameworks, policies, standards, and controls.
- Establish and embed data ownership models, stewardship structures, and accountability frameworks.
- Align data initiatives with enterprise architecture, regulatory requirements, and business strategy.
2. Data Governance & Data Quality Frameworks
- Design and operationalize enterprise data quality frameworks, including:
- Data quality rules definition
- Monitoring and remediation processes
- Data quality scorecards and reporting
- Drive large-scale business rule definition and standardization, including:
- Critical data elements (CDEs)
- Data validation rules
- Data lineage and traceability
- Ensure integration of governance practices into day-to-day operational processes.
3. Master Data Management (MDM) & Reference Data Management (RDM)
- Define and implement enterprise MDM and RDM strategies across domains (e.g., client, product, counterparty).
- Establish authoritative data sources and systems of record for master and reference data.
- Drive clarity on:
- System ownership of master data
- Golden source identification
- Data domain ownership models
- Lead design of target-state MDM architecture and supporting data flows.
- Develop proposals and roadmaps for optimizing MDM and RDM ecosystems.
4. Metadata Management & Data Lineage
- Implement and drive adoption of metadata management frameworks.
- Ensure end-to-end data lineage visibility, including:
- Source-to-consumption traceability
- Business and technical metadata alignment
- Lead usage and configuration of tools to support data cataloguing and lineage.
5. Data Management Tooling & Enablement
- Lead implementation, configuration, and governance of enterprise tools such as:
- Ataccama EDM & CMDM
- Ensure effective integration of tools into data lifecycle processes.
- Drive user adoption and capability uplift across business and technology teams.
6. Target Data Architecture & Data Flow Design
- Define target-state data architecture for MDM and reference data domains.
- Develop end-to-end data flow blueprints, including:
- Ingestion
- Transformation
- Standardization
- Distribution
- Propose ideal-state data ecosystems, balancing:
- Centralization vs federation
- Real-time vs batch processing
- Business vs technical ownership
- Align with enterprise architecture and integration strategies.
7. Stakeholder Management & Leadership
- Partner with:
- Business leaders (Front Office, Risk, Finance, Operations)
- Technology teams
- Data governance councils
- Translate business requirements into data management solutions.
- Lead cross-functional teams to execute large, complex data programs.
- Provide thought leadership on data management best practices.
Key Requirements
Experience
- 10+ years experience in data management, governance, or data architecture within banking or financial services.
- Strong experience in Corporate & Investment Banking (CIB) environments.
- Proven track record of delivering:
- Enterprise data governance frameworks
- Large-scale MDM/RDM implementations
- Data quality and metadata initiatives
Technical & Functional Expertise
- Deep knowledge of:
- Master Data Management (MDM)
- Reference Data Management (RDM)
- Metadata Management & Data Lineage
- Data Quality frameworks
- Hands-on experience with one or more tools:
- Collibra
- Ataccama
- Alation
- Informatica (MDM, DQ, Axon, EDC)
- Strong understanding of:
- Data modelling concepts
- Data architecture principles
- Integration patterns and data flows
Domain Expertise
- Strong understanding of:
- Banking data domains (client, product, trade, risk, finance)
- Regulatory requirements impacting data (e.g., BCBS 239 principles)
- Expertise in:
- Authoritative data sourcing
- Golden source definition
- System ownership and accountability models
Leadership & Delivery
- Experience leading large-scale transformation programs.
- Ability to:
- Define target operating models
- Drive enterprise-wide adoption
- Manage stakeholders at senior levels
- Strong problem-solving and strategic thinking skills.
Qualifications
- Bachelor’s degree in:
- Computer Science, Information Systems, Finance, or related field
- Postgraduate degree or certifications (advantageous):
- Data Management / Data Governance certifications (e.g., DAMA-DMBOK, CDMP)
- TOGAF or architecture certifications
Key Competencies
- Strategic thinking & enterprise mindset
- Strong analytical and conceptual ability
- Stakeholder engagement & influencing
- Communication (business & technical)
- Leadership & team development
- Delivery focus and execution excellence
Success Measures
- Establishment of clear authoritative data sources across domains
- Improved data quality scores and reduced data incidents
- Adoption of governance frameworks and tools
- Delivery of target-state MDM & RDM architecture
- Increased trust in enterprise data assets
Ideal Candidate Profile
A senior, technically strong data leader who combines:
- Deep banking domain expertise
- Hands-on data management tooling experience
- Strategic architectural thinking
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