MEL Data Science Officer, Global MEL

Job Description

The MEL Data Science Officer is part of the Global MEL team and strengthens Orbis’s Monitoring, Evaluation and Learning (MEL) systems through data engineering, analytics, automation, and data governance. The Officer supports the integration of MEL data across systems and countries, enabling accurate, timely and actionable program performance information. The role enhances Orbis’s global data infrastructure and strengthens the organization’s ability to use data for decision-making, accountability and learning.

The MEL Data Science Officer supports Orbis’s global MEL function by designing and maintaining data pipelines, automated reporting systems, and analytical solutions that enhance MEL data quality, accessibility, and interoperability. This role builds standardized data models, maintains Dataverse structures, implements ETL processes, and develops Power BI dashboards that support monitoring, reporting and performance analysis across Orbis’s global programs.

This is a hands-on technical role requiring expertise in Power BI, Dataverse, SQL, Python, Azure Synapse, Power Automate, and data engineering. The Officer collaborates with MEL teams, program teams and the IS/IT unit to ensure that MEL data systems are reliable, efficient, and aligned with global reporting requirements. This role does not supervise staff but provides high-level technical support, training and troubleshooting to MEL stakeholders globally.

REPORTING & WORKING RELATIONSHIPS

The MEL Data Science Officer reports to the Advisor, Data Insights and Solutions (Global MEL) and collaborates closely with:

• Global MEL Team
• IS/IT Team
• Country MEL Officers and Program Teams
• Other data users across Global Programs

Duties and Responsibilities
1. Data Architecture, Engineering & Integration

Design, document and maintain scalable data pipelines connecting MEL datasets across Spectrum, Dataverse, Azure Synapse and other systems.
Develop standardized MEL data models and schema aligned with Orbis’s global indicators and results frameworks.
Implement ETL/ELT processes using SQL, Python, Azure Synapse, Databricks and Data Lake.
Maintain Dataverse tables, relationships, business rules and security structures for MEL data.
Apply data governance practices including version control, documentation, validation scripts and audit trails.
Collaborate with IT/IS teams to ensure secure, reliable data architecture and interoperability between systems.
2. Data Automation, Quality Assurance & Optimization

Conduct routine data validation, completeness checks and integrity assessments within MEL systems.
Develop automated scripts and routines (SQL, Python, DAX, VBA) to support MEL reporting and anomaly detection.
Build Power Automate workflows, AI Builder models and automation pipelines that streamline MEL processes.
Maintain metadata documentation, data dictionaries and SOPs to enhance data transparency and traceability.
Monitor and optimize data refresh cycles in Dataverse and Power BI.
Support predictive analytics or AI-assisted MEL use cases where appropriate.
3. Data Visualization, Dashboards & Reporting

Design, build and maintain Power BI dashboards for monitoring program performance, indicators and outcomes.
Implement advanced dashboard features including role-level security, drill-through functionality, advanced DAX, and optimized data models.
Develop standardized visualization templates for quarterly and annual MEL reporting.
Ensure accuracy, consistency and usability of dashboard outputs through rigorous validation.
Troubleshoot dashboard performance and refresh issues across multiple countries and teams.
4. Stakeholder Collaboration, Technical Support & Capacity Building

Provide hands-on technical support to MEL and country teams on data extraction, dashboard use, indicator mapping and system navigation.
Conduct training sessions on Power BI, Dataverse, SQL, and MEL data workflows for MEL officers and program teams.
Troubleshoot user issues and provide timely technical assistance to ensure smooth tool adoption.
Collaborate with program and MEL teams to co-create tailored analytical solutions.
Support MEL learning initiatives by preparing data-driven insights, visualizations and performance summaries.
Job Requirements
QUALIFICATIONS & EXPERIENCE
Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, Statistics, or related technical field.
4–7 years of experience in data engineering, analytics, automation or enterprise systems integration.
Proven hands-on experience with Power BI, Power Automate, Dataverse, Power Apps and the Microsoft Power Platform.
Strong expertise in SQL, Python, ETL design, data modeling and data warehousing.
Experience with Azure ecosystem (Azure Synapse, Data Lake, Azure Functions, Azure Storage).
Familiarity with data governance, data validation, metadata management and API integrations.
Experience developing automated reporting systems and dashboards supporting program monitoring.
Experience working with global health, development or multi-country program data is an advantage.
Strong communication skills in English.
SKILLS & ABILITIES

Strong foundation in data engineering, ETL pipelines and cloud integration.
Advanced proficiency in Power BI, DAX, and interactive dashboard development.
Ability to translate complex technical concepts into easy-to-use dashboards and tools.
High attention to detail, accuracy and data integrity.
Strong analytical and diagnostic problem-solving skills.
Ability to work collaboratively with technical and non-technical teams across regions.
Proactive and solutions-focused, with the ability to manage multiple priorities.
Enthusiasm for emerging technologies, automation and continuous improvement.
Ability to support and train global teams with varying levels of data literacy.
How To Apply
Apply through

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Fields Of Study

Information System
Computer science
Skills Required

computer science