Information Systems Assistant (Data analyst)

MONUSCO/MONUC was established in 1999 under the auspices of the UN Department of Peacekeeping Operations (DPKO). SCR 2277 (2016) requests MONUSCO to support efforts of the Government of DRC towards the protection of civilians, through a comprehensive approach involving all components of MONUSCO, including through reduction of the threat posed by Congolese and foreign armed groups and of violence against civilians, including sexual and gender-based violence and violence against children to a level that can be effectively managed by the Congolese justice and security institutions; and to support Stabilization through the establishment of functional, professional, and accountable state institutions, including security and judicial institutions. The project aims at a better understanding of the most serious violations of human rights law and international humanitarian law in DRC and supports the leadership of the Mission through informed policy and decision making on future vetting of security forces through risk assessment and mitigation, joint planning for MONUSCO operations, as well as advocacy efforts.

MONUSCO/MONUC was established in 1999 under the auspices of the UN Department of Peacekeeping Operations (DPKO).

Within the delegated authority and under the direct supervision of the Chief I&T or designated authority, the UN Volunteer will undertake the following duties:

Business analysis and stakeholder engagement
• Engage mission sections, pillars and other stakeholders to identify operational problems, decision-support needs and opportunities for data-driven improvement.
• Lead or support requirements-gathering activities, including process mapping, data discovery, gap analysis, feasibility assessment and definition of measurable success criteria.
• Translate business requirements into solution designs, prototypes, implementation plans and realistic estimates of resources, dependencies, risks and delivery timelines.
• Advise stakeholders on suitable analytical methods, visualizations and data-storytelling approaches, while constructively challenging requests that do not address the underlying decision need.

Data engineering and analytics infrastructure
• Design, build, test and maintain reliable data extraction, ingestion, transformation and loading pipelines for structured and semi-structured data from databases, spreadsheets, flat files, APIs and other approved sources.
• Develop scalable data models, curated datasets and data-store structures that support consistent reporting, reuse and future advanced analytics.
• Integrate data from multiple operational systems, implement incremental and scheduled processing where appropriate, and reduce unnecessary manual handling.
• Apply sound software and data engineering practices, including modular development, source control, testing, error handling, logging, documentation, deployment and maintainability.
• Assess existing analytics infrastructure and recommend practical improvements to architecture, performance, resilience and cost-effectiveness within the available UN technology environment.

Business intelligence and advanced analytics
• Design and develop basic to complex reports, dashboards, semantic models and analytical applications using Power BI and other approved tools.
• Conduct exploratory, diagnostic and statistical analysis of large and complex datasets to identify patterns, anomalies, trends, risks and operational improvement opportunities.
• Develop meaningful indicators and analytical frameworks in collaboration with business owners, ensuring that definitions, calculations and limitations are transparent.
• Optimize data models, queries and reports for performance, usability, accessibility and reliable refresh.
• Present findings through concise narratives, visualizations and recommendations tailored to technical, operational and senior leadership audiences.

Data governance, quality and protection
• Establish and apply data-quality controls, validation rules, reconciliation processes and issue-resolution procedures across analytics products.
• Contribute to data dictionaries, metadata, lineage documentation, ownership arrangements, access controls, retention practices and common standards.
• Ensure that solutions are developed in accordance with applicable UN information security, privacy, confidentiality and record-management requirements.
• Promote responsible access to data and design solutions that appropriately separate sensitive information according to user roles and operational need.

Project delivery, adoption and knowledge transfer
• Plan and coordinate assigned analytics initiatives, maintaining clear scope, milestones, risks, dependencies, stakeholder communications and delivery documentation.
• Develop prototypes and minimum viable solutions, coordinate testing and user acceptance, incorporate feedback and support a controlled transition into operational use.
• Monitor adoption and effectiveness after deployment and recommend improvements based on user feedback, usage evidence and changing operational needs.
• Provide technical guidance, peer review, reusable standards, documentation, demonstrations and hands-on knowledge transfer to colleagues and business users.
• Collaborate with technical specialists within MONUSCO, RSCE, other UN entities and external partners to adopt relevant standards and good practices.

Innovation and responsible use of AI
• Monitor relevant developments in artificial intelligence, automation and advanced analytics and identify practical use cases that could add measurable operational value.
• Assess the data readiness, governance, security, ethical considerations, human oversight and stakeholder adoption requirements of proposed AI-enabled solutions.
• Support carefully scoped proofs of concept using approved tools and methods, documenting benefits, limitations, risks and prerequisites before recommending broader implementation.
• Demonstrate continuous learning and the ability to investigate unfamiliar technical challenges, test options systematically and develop practical solutions.
• room technologies.

Expected Results
• Reliable, documented and maintainable data pipelines and integrated datasets established for priority analytical use cases.
• Delivery lead times and engineering bottlenecks are reduced, allowing multiple priority analytics initiatives to progress concurrently.
• Reusable data models, standards and governance controls improve consistency, traceability and trust in mission reporting.
• Analytical products address clearly defined decision needs, perform reliably and are adopted by intended users.
• Manual effort and avoidable data-processing delays are reduced through appropriate automation and streamlined workflows.
• Technical documentation, knowledge transfer and peer review strengthen the sustainability of the Data Analytics function.
• A practical roadmap identifying when and how advanced analytics or AI can be introduced responsibly, based on data readiness and operational value.