Data Analyst

About the Role

The Data Analyst will support Vermi-Farm Initiative's MEL function by turning operational, program, farmer, sales, and financially relevant data into accurate analysis and useful insight. Reporting to the MEL Officer, the role will help strengthen data workflows, data quality, dashboards, routine reporting, and analytical processes.

The position is intended to make data easier to trust and easier to use, while supporting the organization to understand farmer journeys, program performance, commercial activity, and operational trends.

What You Will Do

• Maintain reliable data systems: Support the MEL Officer in maintaining reliable data systems, datasets, analytical workflows, and reporting processes.
• Clean, validate, and reconcile data: Work with data from farmer registration, program activities, sales, finance, digital platforms, field teams, and other approved sources.
• Run routine data quality checks: Document errors, inconsistencies, missing values, duplicates, and the correction actions taken.
• Build dashboards and reports: Develop and maintain dashboards, reports, data summaries, and visualizations for management, MEL, programs, operations, and other authorized users.
• Analyze farmer and commercial performance: Analyze enrollment, participation, service uptake, retention, productivity, transactions, sales, and other agreed performance indicators.
• Support learning analysis: Support the MEL Officer with cohort analysis, trend analysis, comparison of intervention pathways, and other analytical work for learning and decision-making.
• Translate findings into action: Turn analysis into clear summaries, charts, tables, and practical recommendations for non-technical stakeholders.
• Improve data collection: Identify recurring data-entry problems and help improve forms, validation rules, data definitions, and workflows.
• Document your work: Maintain clear documentation of datasets, indicators, calculations, assumptions, data sources, and reporting logic.
• Work with Product/Technology: Support data integrations, exports, database queries, system improvements, and reporting requirements where appropriate.
• Reconcile with Finance and Operations: Reconcile relevant operational and financial data and flag material discrepancies for review.
• Support periodic reporting: Help prepare MEL reports, management packs, board materials, partner reports, and other evidence products as directed by the MEL Officer.
• Protect data confidentiality: Protect the confidentiality and integrity of applicant, farmer, customer, staff, and program data, and follow approved data-access procedures.
• Strengthen the data culture: Help staff understand definitions, dashboards, indicators, and the responsible use of data.
• Other analytical assignments: Undertake other analytical and data-management assignments that support MEL and organizational learning priorities.

Who We're Looking For

A Bachelor's degree in Statistics, Data Science, Economics, Mathematics, Information Technology, Computer Science, Monitoring and Evaluation, Business Analytics, or another strong analytical discipline is preferred. A relevant diploma combined with strong practical analytical experience may also be considered.

• Two to four years of relevant experience in data analysis, MEL, business intelligence, reporting, research, or a related area
• Strong Excel or Google Sheets skills and practical experience with structured datasets
• Experience with one or more of Power BI, Tableau, SQL, Python, R, SPSS, Stata, or similar tools
• Experience cleaning, validating, reconciling, and analyzing data from multiple sources
• Strong attention to detail and a disciplined approach to data quality
• Ability to build clear dashboards and explain findings to non-technical users
• Good understanding of descriptive statistics, trends, segmentation, comparisons, and basic analytical methods
• Strong written and verbal communication skills
• Ability to work with field teams and understand practical data-collection realities
• High standards of confidentiality, accuracy, integrity, and responsible data use