Program Data and Analytics Expert

Your responsibilities

A. MEAL Data Systems and Governance 

• Design and maintain MEAL data architecture for program monitoring, including indicator libraries, codebooks, metadata, naming conventions, and version control.
• Translate logframes, theories of change, and MEAL plans into clear data structures that support monitoring, accountability, reporting, and learning.
• Set up and maintain a secure program data repository for MEAL datasets, tools, forms, dashboards, and documentation, including access controls and clear ownership.
• Develop and apply data governance procedures covering consent language, lawful basis where relevant, data minimization, retention, secure storage, and safe sharing.
• Create practical SOPs for the full MEAL data cycle: collection, cleaning, validation, storage, analysis, publication, archiving, and deletion.
• Support partner alignment with minimum MEAL data standards, common templates, and light-touch quality checks.

B. Programme Analytics and Decision Support 

• Turn program questions and MEAL frameworks into analysis plans linked to concrete decision points such as targeting, seasonality, pipeline planning, accountability, and program adaptation.
• Develop and maintain dashboards and analysis packs using Power BI, Tableau, Excel, or similar tools for program performance, outcome trends, quality flags, and management review.
• Introduce automated and semi-automated data quality checks, including outlier rules, duplicate detection, missing values, inconsistent coding, and missing disaggregation flags.
• Produce short decision products such as learning briefs, analysis notes, management snapshots, and “what changed” summaries using quantitative and qualitative data.
• Support proposal development, reporting, and evaluations with indicator logic, disaggregation plans, baseline values, assumptions, benchmarks, and data evidence.
• As required, conduct deeper analysis including pre/post comparisons, cohort tracking, basic forecasting, risk signals, or vulnerability trend analysis, where data quality allows.

C. Digital MEAL Tools and Field Workflows 

• Lead the design, testing, and quality assurance of digital data collection tools, including CommCare, Kobo, ODK, or similar platforms.
• Build forms with appropriate skip logic, constraints, translations, validation rules, and user-friendly workflows for field teams and partners.
• Embed privacy-by-design into digital tools by limiting personal data, applying role-based access, and promoting secure device and data handling practices.
• Train enumerators, MEAL staff, program staff, and partners on digital workflows, field protocols, informed consent, and common error prevention.
• Create rapid feedback loops from field data through daily or weekly checks, issue logs, corrective action tracking, and support to field teams.
• Maintain reusable question banks, form templates, standard disaggregation fields, and documentation to improve consistency across projects.

D. Accountability, Feedback and Learning Analytics 

• Strengthen complaints and feedback mechanisms by improving data capture, categorization, referral tracking, resolution monitoring, and closing-the-loop analysis.
• Ensure safe handling of sensitive feedback, including safeguarding-related cases, with clear confidentiality rules, restricted access, and referral pathways.
• Analyze feedback trends such as themes, volumes, resolution times, geographic patterns, sensitive categories, and recurring program issues.
• Produce actionable insights from accountability data through feedback and compliant mechanisms to inform program adaptation, management decisions, and learning discussions.

E. Evaluations, Learning and Evidence Products 

• Support and/or contribute the development of a learning agenda and help teams identify priority evidence questions related to relevance, effectiveness, inclusion, accountability, and program quality.
• Contribute to baseline, endline, evaluation, and study design by strengthening data collection tools, data quality standards, and analysis plans.
• Support the use of evaluation and monitoring findings through concise evidence products, decision notes, learning briefs, and action tracking.
• Facilitate data review sessions, after-action reviews, and learning workshops that help teams interpret evidence and agree on practical follow-up actions.

F. WHH GDPR Commitments and Responsible Data Protection 

• Act as the country office focal point for responsible program data management and GDPR-aligned practices within MEAL, in coordination with management and relevant support functions.
• Maintain practical data protection documentation for key MEAL and program datasets, including data inventories, access lists, retention/deletion schedules, and issue logs where applicable.
• Support data processing agreements and partner due diligence inputs for program data systems, where required.
• Advise teams on safe data sharing for donor reporting, evaluations, dashboards, accountability mechanisms, and learning products, including anonymization or pseudonymization where needed.
• Build staff and partner awareness on confidentiality, informed consent, responsible data use, and safe handling of sensitive program and feedback data.

G. Capacity Building and Change Management 

• Strengthen capacity of MEAL, program, and partner staff on data literacy, reading dashboards, understanding quality flags, and ways of using data for decisions.
• Build practical skills on data quality, sampling basics, digital tools, analysis of workflows, and responsible data management.
• Promote a culture of evidence use through regular program review meetings, data-to-action routines, and simple follow-up tracking.
• Support change management by introducing tools and processes in a practical, user-centered way that fits field realities and partner capacities.

Responsible Use of AI and Advanced Analytics 

• AI or advanced analytics may be used as support tools for program data analysis, such as pattern detection, text tagging, data quality review, or drafting narrative summaries, only when the following principles are respected:
• Sensitive data is protected and all processing complies with WHH data protection, safeguarding, and responsible data requirements.
• Human review is maintained before any decision, reporting, or external sharing of outputs.
• Methods are documented and explainable, including what data was used, what rules or models were applied, and what limitations exist.
• AI is not used to make eligibility, targeting, or assistance decisions without approved governance, clear safeguards, and management approval.

Your profile

Minimum Requirements 

• MSc or BSc. In Computer Science/Information Management Science/Data Science
• At least 5 years of relevant experience in programme data management, information management, research, program quality, analytics, and/or MEAL in humanitarian or development settings.
• Strong understanding of MEAL frameworks, including logframes, theories of change, indicators, Data Quality Dimensions and Assessments, evaluations, accountability, and learning processes.
• Demonstrated experience producing analytical products that influenced programme or management decisions, such as dashboards, analysis packs, decision briefs, or learning products.
• Strong practical experience with digital data collection tools such as CommCare, Kobo, ODK, or similar platforms, including form design, Quality Assurance, and field workflows.
• Strong data analysis and visualisation skills, including advanced Excel; experience with Power BI, Tableau, or similar dashboarding tools are required.
• Experience with SQL, Python, R, Power Query, or other data cleaning and analysis tools.
• Solid understanding of responsible data management, including confidentiality, informed consent, data minimisation, retention, safe sharing, and access control.
• Ability to explain data and analysis clearly to non-technical colleagues and build confidence in using evidence for programme decisions.
• Experience training, mentoring/coaching staff and partners on MEAL data systems, digital tools, data quality, or data use.
• Excellent spoken and written English and Amharic.
• Willingness to travel to project implementation areas.

Desirable Assets

• Experience designing and analyzing feedback and compliant data.
• Experience supporting partner capacity strengthening and harmonising tools across multiple projects.
• Experience with data governance, data protection, or GDPR-aligned programme data practices in an INGO or comparable setting.
• Familiarity with responsible AI principles, including bias, privacy, explainability, human review, and documentation.
• Experience developing country-level or portfolio-level program data models, indicator libraries, or management dashboards.

Competency 

• Strong analytical thinking and problem solving.
• Strong understanding of MEAL, accountability, and program quality.
• Data quality orientation and attention to detail.
• Clear communication, facilitation, and capacity building skills.
• Ability to work across MEAL, program, operations, partners, and support functions.
• Practical judgement in balancing technical quality with field realities.
• Planning, prioritization, and delivery under deadlines.
• Integrity, confidentiality, and safeguarding-minded behavior.

Candidate Profile 

• The ideal candidate combines strong programme data, analytics, and digital systems experience with strong MEAL knowledge. He/She does not need to be an IT infrastructure specialist, but should be confident in designing practical data workflows, improving data quality, building useful analysis products, and helping teams use evidence for better programme decisions.

Our offer

We offer you the opportunity to work in a responsible and interesting field as part of an extremely dedicated team. Welthungerhilfe attaches great importance to the personal and professional development of its employees. Remuneration is based on our gender-independent salary scale.