Senior MEAL Expert

Organization Profile and Background Welthungerhilfe is an international NGO with Head-Quarter in Bonn and working in Africa, Asia, and Latin America. Welthungerhilfe is fighting to help free the world of hunger and poverty. This vision is the basis of our organization’s character and activities. Welthungerhilfe secure funds from private donations and international funding organizations, for example, EC, UN-OCHA, ECHO, and German Government. We work in the sectors of Rural Development, WASH, Natural Resource Management and Social Development. Welthungerhilfe Ethiopia follows partnership approach for project planning, implementation, and administration with Ethiopian Partner-NGOs and in exceptional cases WHH implements projects by itself. Senior MEAL Expert Location: Addis Ababa, Ethiopia  Contract: Initial contract 1 years (with strong prospects for extension)  Eligibility: Open to Ethiopian nationals only  What we offer  Fair compensation: Transparent salary structure based on WHH salary scale  Well‑being & duty of care: Strong focus on safety, security, and staff health  Modern work environment: Active investment in digitalization, systems, and innovation  Learning & development: Opportunities for professional growth and leadership development  Purpose‑driven team: Work with committed and diverse colleagues striving for a world without hunger  Purpose of the role   The position is to be filled as soon as possible, with an initial contract duration of one year. There are very good prospects for an extension. Employment location will be Addis Ababa, Ethiopia. To support evidence-based programming by building practical data systems, ensuring high-quality and responsible data management, and translating programme data into clear insights that improve decision-making and programme outcomes. Your responsibilities A standardized country data model for key programmes (indicator library, disaggregation, definitions, codebooks).  Dashboards and monthly/quarterly decision products used by programme and management teams. Routine automated data quality checks (completeness, consistency, outliers, duplicates) implemented and documented.  A digital data collection approach (forms, training, field workflows) that reduces errors and speeds up reporting.  A feedback/CFM dataset and trend analysis approach that supports accountability and learning (-safe).  A simple -aligned data governance package (SOPs, templates, retention rules, access controls) adopted by projects and partners.  Mian Responsibilities   A. Data Systems & Governance (MEAL-owned)  Support the development and harmonization of project-specific MEAL plans, indicator tracking tools, reporting templates, and monitoring schedules in collaboration with programme teams and partners.  Design and maintain a MEAL data architecture for programme monitoring (indicator library, codebooks, metadata, and version control).  Set up and maintain a secure programme database/structured repository for MEAL datasets, tools, and documentation (including access control and naming conventions).  Develop and enforce data governance procedures: lawful basis/consent language, data minimisation, retention schedules, secure storage, and safe sharing rules.  Create SOPs for the full data cycle: collection, cleaning, validation, storage, analysis, publication, and archiving/deletion.  Support partner data systems alignment (minimum standards, tool templates, and light-touch audits).  B. Analytics & Decision Support  Analyse monitoring data and provide actionable recommendations to programme teams and management to support adaptive project management and evidence-based decision-making.  Contribute to donor and internal reporting through verification and analysis of indicator progress and supporting documentation.  Translate logframes/ToCs into analytical questions and build analysis plans aligned to decision points (e.g., targeting, seasonality, pipeline planning).  Develop and maintain dashboards and analysis packs (Power BI/Tableau/Excel) for programme performance, outcome trends, and quality flags.  Implement automated data checks (outlier rules, duplicate detection, missing disaggregation flags) and document the logic for maintainability.  Run deeper analyses when needed: pre/post comparisons, cohort tracking, basic forecasting or risk signals (only where data quality supports it).  Support proposal design and reporting with indicator logic, disaggregation plans, and data evidence (baseline values, assumptions, benchmarks).  C. Digital Data Collection & Tooling  Support the development and maintenance of project monitoring databases including digital beneficiary master lists and distribution tracking systems where applicable.  Lead the design and QA of digital data collection tools (CommCare), including skip logic, constraints, and translations.  Embed -by-detools (minimum necessary fields, role-based access, secure device/data handling).  Train enumerators and programme teams on digital workflows, field protocols, informed consent, and common error prevention.  Create rapid feedback loops from field data: daily/weekly checks, issue logs, and corrective actions.  Maintain a library of reusable forms, question banks, and standard disaggregation fields to improve consistency across projects.  D. Accountability, Feedback & Learning Analytics  Support the implementation and monitoring of WHH accountability standards, including Feedback and Complaints Response Mechanisms (FCRM), and ensure alignment with safeguarding and Code of Conduct requirements.  Strengthen complaints and feedback mechanisms (CFM): data capture, categorisation, referral workflows, and closing-the-loop tracking.  Ensure safe handling of sensitive feedback (including safeguarding-related cases) with clear confidentiality rules, access restrictions, and referral pathways.  Analyse feedback trends (themes, volumes, resolution times, sensitive categories) and produce actionable insights for programme adaptation.  Where appropriate and safe, apply basic text analytics to feedback comments (topic tagging/trend detection) with strict controls and human review.  E. Evaluations, Learning & Evidence Products  Monitor and support the implementation of evaluation recommendations and ensure lessons learned are integrated into programme improvement and future project design.  Develop a learning agenda and help teams plan evaluations and studies that answer priority questions (relevance, effectiveness, inclusion, accountability).  Ensure baselines, endlines, and evaluations follow appropriate methods and produce usable recommendations.  Produce short evidence products: learning briefs, decision notes, and ‘what changed’ summaries combining quantitative and qualitative data.  Facilitate after-action reviews and learning workshops, and support action tracking.  F. WHH Commitments & Data Protection Lead (Country Office)  Act as the country office focal point for programme data protection and -aligned practices in coordination with management and relevant support functions.  Maintain practical data protection documentation for MEAL (data inventories for key datasets, access lists, retention/deletion schedules, and incident/issue logs as applicable).  Support data processing agreements and partner due diligence inputs for programme data systems (as required), and advise teams on minimum safeguards.  Provide guidance on safe data sharing for donor reporting, evaluations, and learning products (anonymisation/pseudonymisation where needed).  Support staff capacity building on data protection, confidentiality, and responsible data use across projects and partners.  G. Capacity Building & Change Management  Assess MEAL capacity gaps among partners and programme teams and support the development and implementation of MEAL capacity strengthening plans.  Facilitate reflection meetings, learning sessions, and cross-project knowledge sharing within the Country Office and with implementing partners.  Coach MEAL and programme staff (and key partners) on data literacy: interpreting indicators, reading dashboards, and asking good questions of data.  Build practical skills on data quality, sampling basics, digital tools, and analysis workflows.  Promote a culture of data use through regular review meetings and simple ‘data-to-action’ routines.  4. Responsible Use of AI (Guiding Principles)  AI may be used as a support tool for analytics (e.g., pattern detection, text tagging, drafting narrative summaries) only when:  Sensitive data is protected and processing complies with WHH data protection and safeguarding requirements.  Outputs are checked by humans before any decision, reporting, or external sharing (human-in-the-loop).  Methods are documented and explainable (what data, what rules/model, what limitations).  AI is not used to make eligibility/assistance decisions without approved governance and clear safeguards.