Data Quality Assurance Impact Evaluation Lead - M&E (Kumasi, ASHANTI REGION, GH, P. O. BOX )

About the Role

The Data Quality Assurance Lead - Monitoring & Evaluation (M&E) is a pivotal role within the Monitoring & Evaluation department. This role is responsible for ensuring the integrity, accuracy, and reliability of data across all M&E processes. Positioned at the intersection of data management and program evaluation, the role collaborates closely with internal stakeholders such as the data analytics team, program managers, and external partners to implement and maintain robust data quality assurance frameworks. Additionally, this role will oversee our Impact Evaluation study, driving research and data analysis to support sustainable agricultural practices.  The role offers opportunities to lead critical data initiatives that drive impactful decision-making and contributes to the continuous improvement of data-driven practices within the organization

Key Responsibilities

• Data Quality Assessments: Conduct regular data quality checks across programs to identify discrepancies and areas of improvements. Oversee data cleansing, validation, and integrity checks to maintain high data quality.
• Data Management & Governance: Coordinate with Stakeholders to develop SOPS and Training Materials for data collection
• Monitoring & Evaluation Support: Collaborate with M&E teams to design and implement data collection tools and methodologies.
• Training & Support: Provide training and support to Operation and MEL team on data quality best practices and tools.
• Data Analysis: Analyze Program data to generate insights and reports for stakeholders.
• Process Improvement: Lead initiatives to automate data quality processes and integrate advanced data management technologies
• Audit processes: Lead all internal and external audits
• Audit Data: Prepare and conduct quality check on all dataset in the right template for submission
• Field Activity Monitoring and Verification

• Plan all field monitoring and verification activities.
• Prepare field reports after monitoring and verification excises
• Conduct deep dive studies where they need to be.

Impact Evaluation lead

1. Research & Analysis:
   • Design and lead Project Impact Evaluation study, focusing on sustainable agricultural practices and data-driven decision-making.
   • Develop research methodologies, gather and analyze data, and interpret findings to inform strategic decisions.
2. Stakeholder Engagement:
   • Engage with internal and external stakeholders, including farmers, partners, and research institutions.
   • Facilitate workshops, meetings, and presentations to share study results and gather feedback.
3. Project Management:
   • Plan, coordinate, and oversee all aspects of the Agrilogic study, ensuring timely delivery of milestones and objectives.
   • Manage budgets, timelines, and resources effectively to achieve project goals.

About You

• Master’s degree in Monitoring and Evaluation, Data Science, Statistics, Agriculture, Environmental Science, or a related field.
• Minimum of 4 years of experience in data quality assurance or agricultural research, with at least 2 years’ experience within Monitoring & Evaluation.
• Deep knowledge of data management best practices and M&E frameworks
• Proficient in data validation, data cleaning, and data governance processes.
• Proficiency in data management tools with experience with statistical analysis software (e.g., SQL, Python, SPSS Excel, R and Power BI) and data visualization tools (e.g., Tableau, Power BI).
• Working knowledge of Evaluation methods such as Randomized Controlled Trials (RCTs), Difference-in-Differences (DiD) etc.
• Strong analytical and problem-solving skills with a high level of attention to detail.
• Excellent communication and leadership skills, capable of managing cross-functional teams.
• Proactive and innovative problem solver with the ability to drive continuous improvement.
• Strong organizational skills and the ability to manage multiple projects simultaneously.
• Willing to travel 65% of the time to field operation areas.