Data Manager
Confidential
Publiée il y a 1 semaine · Expire dans 1 mois
Description du poste
Position Overview
The Ministry of Health, in collaboration with partners, is implementing the research project titled "Evaluating Maternal, Neonatal, and Child Nutrition and Health (MNCNH) Innovation Impact in Rwanda." This project supports the Health Sector Strategic Plan V (HSSP V, 2024–2029) and evaluates high-impact interventions including Multiple Micronutrient Supplementation (MMS), Calcium Supplementation, Intravenous Iron, PPH Bundles, and Point-of-Care Ultrasound (POCUS).
A centralized data warehouse will serve as the backbone for data integration, monitoring, and advanced analytics across all study components and participating health facilities.
We are seeking a highly skilled and detail-oriented Data Manager to oversee all data management functions of the MNCNH Innovation Impact Project. The successful candidate will ensure high-quality data collection, integration, storage, validation, security, and analysis support across all study components. This role requires close collaboration with research, clinical, and technical teams to ensure data integrity and compliance with national data governance regulations.
The position will be seconded to the Ministry of Health.
Key Information
- Location: Ministry of Health, Kigali, Rwanda
- Reports To: Research Coordinator
- Contract Type: Full-time
- Duration: One year renewable contract
Key Responsibilities
- Develop and oversee the project's Data Management Plan, including data flow diagrams, validation rules, and standard operating procedures (SOPs)
- Manage and maintain centralized project databases, ensuring secure storage, backup systems, and disaster recovery mechanisms
- Coordinate integration of data from multiple sources including EMRs (eBuzima, MediSoft), DHIS2, REDCap, WelTel, laboratory systems, and other digital tools
- Ensure completeness, accuracy, and consistency of datasets through routine data quality assessments and validation checks
- Design and implement automated data cleaning procedures
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