UNICEF requires the services of an external consultant to support the Office of Strategy and Evidence (OSE) in enhancing its data engineering, report automation, and AI-based analytics, while simultaneously migrating primary responsibility for its technology stack to the platforms team of the Digital Impact Division (DID).
Responsibilities:
Design and maintain automated data pipelines across UNICEF’s internal (QA and Production Indicator Data Warehouses, Data Bricks, Snowflake, Reference Data Manager, etc.) and external platforms.
Develop and implement automated workflows for flagship reports such as SOWC, ensuring repeatability, transparency, and reduced manual workload.
Prototype and productionize AI-driven solutions using retrieval-augmented generation (RAG), including robust QA methodologies from frameworks such as DeepEval.
Support technical preparations, data flows, and quality checks for indicator data storage and consultations with field office and national partners.
Assist in governance, documentation, and systems handover for technology transition to the DID Platforms team.
Support the Frontier Data Network team with needed analytics and technology.
Provide training and technical documentation to ensure sustainability.
Building and optimizing data pipelines across the data lifecycle using an array of modern cloud services and data architectures, including LLMs, analytics platforms, and data lakehouses.
Developing report automation workflows for flagship publications, including The State of the World’s Children (SOWC) and The State of Africa’s Children (SOAC).
Advancing UNICEF’s use of generative AI across the data lifecycle, from automated data extraction to RAG-based report generation, contextualization, and localization, including designing and implementing QA strategies using emerging AI-testing frameworks.
Supporting indicator data, structuring, storage, and consultation processes with field offices and member states.
Technology and analytics support to the Frontier Data Network as it migrates to DID.
Together with the DID Platforms Team facilitate the technology transition of PaaS, Saas and bespoke full-stack apps built on varying enterprise-grade software languages from the Data and Analytics Section (D&A) of OSE to DID.
Requirements:
Advanced University degree in Software Engineering or a related field.
5+ years of experience developing full stack software products.
5+ years of experience working with data and analytics tools.
5+ years deploying software solutions to cloud environments, including IaaS, Paas, and SaaS.
Proven experience in implementing ETL pipelines over heterogenous data using Python, R and SQL.
Experience discovering and harmonizing development data using scripted approaches for access with both file-based and API methods.
Experience using cloud-based analytics platforms and data and spatial data processing tools, e.g. Data Bricks.
Experience implementing generativeAI solutions, especially RAG with strong understanding of relevant QA and testing approaches.
