Background information about the job or company (e.g., role context, company overview)

The Aga Khan University (AKU) is a private, autonomous and self-governing international University, with 13 teaching sites in 6 countries over three continents (www.aku.edu). An integral part of the Aga Khan Development Network (www.akdn.org), AKU provides higher education inseveral disciplines, carries out research relevant to the countries, and operates 7 hospitals and over 325 outreach clinics, all at international standards. It has close to 4,000 students and 14,000 staff. The University is both a model of academic excellence and an agent of social change. As an international institution, AKU operates on the core principles of quality, relevance, impact, and access.

An excellent opportunity exists for you to join The Aga Khan University, in the position of a Data Manager for Kikohozi Project. This is a full-time position based in Dar es Salaam, Tanzania.

The Data Manager will play a critical role in supporting the scientific and operational success of the Kikohozi Classifier project by ensuring that all data collected is accurate, secure, and ethically managed. This role extends beyond database development to include oversight of data flow from collection to analysis, ensuring compliance with institutional, national, and international standards.

Responsibilities or duties

• Design, develop, and maintain a secure and user-friendly central project database for storing cough sound recordings, clinical data, and metadata from 30,000 participants in collaboration with ETH Lab at MUHAS
• Implement data management tools that support efficient entry, cleaning, retrieval, and quality monitoring.
• Ensure compatibility of project data with AI/ML teams’ requirements for training and validation datasets
• Establish and monitor data quality control procedures to detect and resolve inconsistencies, errors, or incomplete records.
• Conduct regular data audits to maintain integrity and reliability across multiple data collection sites.
• Provide guidance and training to research assistants on standardized data entry and collection protocols.
• Ensure compliance with ethical, legal, and institutional standards, including GDPR-equivalent principles, data-sharing agreements, and participant confidentiality.
• Oversee data encryption, secure storage, and controlled access protocols to protect sensitive participant information.
• Prepare for and facilitate ethical reviews, audits, or inspections relating to data management.
• Process and clean data for analysis, ensuring datasets are well-structured, complete, and ready for statistical and AI/ML modeling.
• Support researchers and analysts by generating summaries, descriptive reports, and preliminary datasets for progress reporting and decision-making.
• Ensure timely delivery of reports on datasets for publications, presentations, and donor reporting
• Train and mentor field research assistants and junior staff on best practices in data collection, management, and security.
• Participate in project meetings to provide updates on data-related challenges, risks, and progress.
• Collaborate closely with project investigators, the AI/ML development team, and partner institutions (AKU, MUHAS ETH Lab, University of Warwick) to align data requirements.
• Contribute to teaching AKU students on data management techniques and provide data management expertise to ongoing AKU research projects to enhance the rigor and reliability of research outputs.

Qualifications or requirements (e.g., education, skills)

• Bachelor’s degree in computer science, Data Science, Biostatistics, Health Informatics, or related field
• Master’s degree (preferred) in Health Informatics, Epidemiology, Biostatistics, or Data Management.
• Professional training or certification in data management, database systems, artificial intelligence and machine learning model development or information security is an added advantage.

Experience needed

• Minimum 2-3 years of progressive project data management experience, including compliance with donor data management frameworks (e.g., UKRI, MRC, or similar).

Any other provided details (e.g., benefits, work environment, team info, or additional notes)

• Technical Skills: Skilled in designing and managing databases (SQL, REDCap, MySQL, PostgreSQL), cleaning and preparing data for large multi-site studies, handling AI/ML data needs including unstructured data, and using visualization tools like Tableau and Power BI.
• Research and Data Governance: Strong grasp of health research data workflows and compliance standards (e.g., GDPR-like regulations, HIPAA). Experienced in data security, access control, ethics approvals, and data-sharing processes for multi-institutional projects.
• Quality Assurance and Compliance: Able to build and apply quality assurance systems, perform data audits and validation checks, and ensure accuracy, integrity, and reproducibility of datasets for AI/ML and clinical research.
• Leadership and Collaboration: Experienced in training and supervising junior data staff and working effectively with multidisciplinary teams across research, clinical, AI/ML, and community settings.
• Communication and Reporting: Strong communicator with the ability to produce clear summaries, technical documents, and reports. Skilled at turning complex data into actionable insights and presenting risks, challenges, and progress to stakeholders.
• Problem-Solving and Adaptability: Capable of anticipating and resolving data challenges in multi-site projects, adapting to new technologies and requirements, and applying strong analytical skills to troubleshoot technical and data issues.
• Professional development workshops
• Scholarship application assistance

• Design, develop, and maintain a secure and user-friendly central project database for storing cough sound recordings, clinical data, and metadata from 30,000 participants in collaboration with ETH Lab at MUHAS
• Implement data management tools that support efficient entry, cleaning, retrieval, and quality monitoring.
• Ensure compatibility of project data with AI/ML teams’ requirements for training and validation datasets
• Establish and monitor data quality control procedures to detect and resolve inconsistencies, errors, or incomplete records.
• Conduct regular data audits to maintain integrity and reliability across multiple data collection sites.
• Provide guidance and training to research assistants on standardized data entry and collection protocols.
• Ensure compliance with ethical, legal, and institutional standards, including GDPR-equivalent principles, data-sharing agreements, and participant confidentiality.
• Oversee data encryption, secure storage, and controlled access protocols to protect sensitive participant information.
• Prepare for and facilitate ethical reviews, audits, or inspections relating to data management.
• Process and clean data for analysis, ensuring datasets are well-structured, complete, and ready for statistical and AI/ML modeling.
• Support researchers and analysts by generating summaries, descriptive reports, and preliminary datasets for progress reporting and decision-making.
• Ensure timely delivery of reports on datasets for publications, presentations, and donor reporting
• Train and mentor field research assistants and junior staff on best practices in data collection, management, and security.
• Participate in project meetings to provide updates on data-related challenges, risks, and progress.
• Collaborate closely with project investigators, the AI/ML development team, and partner institutions (AKU, MUHAS ETH Lab, University of Warwick) to align data requirements.
• Contribute to teaching AKU students on data management techniques and provide data management expertise to ongoing AKU research projects to enhance the rigor and reliability of research outputs.

• Designing and managing databases (SQL, REDCap, MySQL, PostgreSQL)
• Cleaning and preparing data for large multi-site studies
• Handling AI/ML data needs including unstructured data
• Using visualization tools like Tableau and Power BI
• Strong grasp of health research data workflows and compliance standards (e.g., GDPR-like regulations, HIPAA)
• Experienced in data security, access control, ethics approvals, and data-sharing processes for multi-institutional projects
• Able to build and apply quality assurance systems, perform data audits and validation checks, and ensure accuracy, integrity, and reproducibility of datasets for AI/ML and clinical research
• Experienced in training and supervising junior data staff
• Working effectively with multidisciplinary teams across research, clinical, AI/ML, and community settings
• Strong communicator with the ability to produce clear summaries, technical documents, and reports
• Skilled at turning complex data into actionable insights and presenting risks, challenges, and progress to stakeholders
• Capable of anticipating and resolving data challenges in multi-site projects
• Adapting to new technologies and requirements
• Applying strong analytical skills to troubleshoot technical and data issues

• Bachelor’s degree in computer science, Data Science, Biostatistics, Health Informatics, or related field
• Master’s degree (preferred) in Health Informatics, Epidemiology, Biostatistics, or Data Management.
• Professional training or certification in data management, database systems, artificial intelligence and machine learning model development or information security is an added advantage.