Moniepoint Incorporated is a global business payments and banking platform and recently became QED Investors’ first investment in Africa. We are the partner of choice for over 600,000 businesses of all sizes, powering the dreams of SMBs and providing them with equal access to the tools they need to grow and scale.
We are recruiting to fill the position below:
Job Title: Metadata & Data Lineage Specialist
Location: Lagos
About this role
We are looking for a Metadata & Data Lineage Specialist to build out and run the organization's metadata management and data lineage capabilities.
You will be the person who makes our data catalogue genuinely useful — helping teams find, trust, and understand the data they are working with, and tracing exactly how that data moves and transforms across our systems.
You will take our metadata management and data lineage frameworks from paper to practice: growing coverage, improving quality, automating collection wherever possible, and building lineage that holds up for our most critical data products, regulatory submissions, and business-critical flows.
Responsibilities
Metadata Management & Data Cataloguing: Maintain and enhance enterprise metadata, developing business glossaries, data definitions, and ownership frameworks within DataHub. Identify gaps and ensure metadata changes align with governance processes.
Data Lineage Management: Develop, maintain, and validate end-to-end data lineage for critical data flows, products, reports, and customer journeys across source systems, ETL/ELT pipelines, and downstream consumers.
DataHub Platform & Integration: Support the administration and optimisation of DataHub. Configure ingestion workflows, monitor metadata ingestion jobs, and identify opportunities to improve coverage through automation and APIs.
Metadata Quality & Governance: Establish metadata quality rules and KPIs. Perform regular assessments to highlight gaps, support data impact assessments, and ensure alignment with enterprise governance policies.
Data Classification & Privacy: Apply data classification metadata based on sensitivity and regulatory requirements. Support Risk, Compliance, and InfoSec teams with necessary lineage information for assessments.
AI & Advanced Data Discovery: Drive the adoption of AI-assisted metadata management to automate capture, classification, and enrichment. Contribute to the development of semantic models and knowledge graphs.
Stakeholder Engagement: Act as a subject matter expert, facilitating workshops and providing guidance to data producers and consumers to improve overall data literacy and DataHub adoption.
What success looks like in this role
The enterprise data catalogue is accurate, easily discoverable, and heavily utilised by cross-functional teams to understand data assets.
End-to-end data lineage is robust, validated, and actively supports critical data products, regulatory submissions, and incident investigations.
Metadata collection is highly automated, maintaining strict quality standards and governance controls with minimal manual intervention.
Data literacy and platform adoption (DataHub) scale across the organisation through effective stakeholder enablement.
Experience & Background
Bachelor’s Degree in Computer Science, Information Management, Data Science, Information Systems, Library/Information Science, Engineering, or a related discipline.
3–6 years of experience in Metadata Management, Data Governance, Data Architecture, or Data Engineering.
Demonstrable hands-on experience with Solidatus, Informatica, DataHub, or a comparable enterprise data catalog platform.
Practical experience developing end-to-end data lineage across complex data environments.
Deep familiarity with modern data architectures (Medallion, Data Mesh, Data Vault 2.0, warehouses, data lakes, cloud platforms).
Experience in financial services, fintech, payments, banking, or another highly regulated data environment is highly desirable.
Skills & Competencies:
Strong proficiency working with technical and business metadata, business glossaries, data domains, and data classification.
Experience working with relational databases and a solid understanding of SQL and database structures.
Knowledge of APIs, ETL/ELT pipelines, and automated metadata integration.
Familiarity with AI-assisted metadata management, semantic modelling, ontologies, and knowledge graph concepts.
Strong stakeholder management skills with the ability to facilitate training and influence adoption.
Professional certification in Data Management or Architecture (e.g., DAMA CDMP, CIMP, DCAM) or related platform certifications are advantageous.
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