ACS/Data Engineers
Organizational Context
The United Nations Economic Commission for Africa (ECA), in partnership with the United Nations Statistics Division (UNSD), is implementing a project to strengthen Africa's statistical ecosystem. This role supports ECA's capacity to transform data into actionable insights through a robust analytics and AI layer built on the UN Data Commons, enhancing data integration and usability across the continent.
Job Purpose
This role provides hands-on technical support across the data lifecycle, from pipeline to policy, within the "Advancing Data Integration, Innovation and Capacity in Africa" project. The primary purpose is to design, implement, and maintain data engineering workflows, databases, and AI-enabled applications. This includes integrating regional and national datasets, ensuring data quality, optimizing database performance, developing analytical hubs, and creating an AI-driven prototype for policy briefs, ultimately enhancing ECA's data-driven decision-making capabilities for Africa.
Responsibilities
Design and implement core data engineering workflows, including the integration of 7-12 high-impact regional and national datasets into the UN Data Commons schema. Collect, structure, clean, and preprocess datasets from multiple sources, ensuring completeness, consistency, and accuracy according to data quality and governance standards. Translate technical specifications into data models and database outputs, optimize database performance, and maintain database objects. Co-design, develop, and test two interactive, web-based Thematic Analytical Hubs with advanced visualization and reporting features. Develop an AI-enabled prototype for data-driven policy briefs and an AI-enabled data harvesting system for African statistical publications and datasets. Collaborate with domain experts and technical teams to translate user needs into technical solutions and produce clear technical documentation.
Work Experience
A minimum of 2 years of relevant work experience in statistics, engineering, information systems, or a related field is required. Extensive experience with the entire data lifecycle, including frontend/backend web development, data engineering, analysis, and visualization, is essential. Experience managing large datasets and complex data engineering problems is desirable.
Skills
Data engineering, data analysis, AI-enabled applications, database management, ETL/ELT processes, data modeling, Python, SQL, frontend technologies (JavaScript, HTML/CSS, TypeScript), dashboard/visualization tools (Power BI, Tableau), open-source tech stacks.