Data Quality Assurance Impact Evaluation Lead

Overview

One of the most important positions in the Monitoring & Evaluation division is the Data Quality Assurance Lead (M&E). In all M&E processes, this function is in charge of guaranteeing the dependability, correctness, and integrity of data. Situated at the nexus of data management and program evaluation, the function works closely with program managers, external partners, and the data analytics team among other internal stakeholders to establish and maintain strong frameworks for data quality assurance.

Responsibilities

• Data Quality Assessments: To find inconsistencies and potential improvement areas, regularly review the quality of the data across all programs. To ensure good data quality, supervise the validation, integrity checks, and cleaning of the data. • Data Management and Governance: Work with Stakeholders to Create Training Materials and SOPS for Gathering Data. • Observation & Assessment Support: Work together with M&E teams to develop and put into practice data collecting techniques and technologies. • Training & Support: Give the Operation and MEL team guidance on data quality best practices and resources. • Data analysis: Examine program data to produce reports and insights for interested parties. • Process Improvement: Take the helm of projects to include cutting-edge data management technology and automate data quality procedures. • Oversee all internal and external audit procedures. • Audit Data: Assemble and verify all datasets using the appropriate template before submitting them. • Field Activity Monitoring and Verification: Arrange for every activity related to field monitoring and verification. • After monitoring and verification exercises, prepare field reports. • Perform in-depth research when it is necessary.

Requirements

• A master's degree in environmental science, agriculture, data science, monitoring and evaluation, statistics, or a related discipline. • 4 years minimum of experience in agricultural research or data quality assurance, including a minimum of 2 years in monitoring and evaluation. • Thorough familiarity with M&E principles and best practices for data management • Competent in data governance, data cleansing, and validation procedures. • Proficiency with data management tools and familiarity with data visualisation tools (such as Tableau, Power BI) and statistical analysis software (such as SQL, Python, SPSS Excel, R, and Power BI). • Working familiarity with evaluation techniques like Difference-in-Differences (DiD) and Randomised Controlled Trials (RCTs). • Strong attention to detail, analytical, and problem-solving abilities. • Strong leadership and communication abilities; competent to oversee multidisciplinary teams.