Geospatial Data Engineer; Climate and Connectivity

Job Highlight

As countries increasingly face climate and disaster-related risks, the resilience of schools and education systems is critical. The Geospatial Data Engineer will support a pilot initiative to test and demonstrate how hazard and climate data can be integrated with DI’s school geolocation and connectivity datasets.

The Geospatial Data Engineer will identify and process relevant hazard-related datasets and integrate them with DI’s school geolocation and connectivity datasets. The role will identify and pilot use cases in at least two emergencies for school-level risk and the impact on connectivity. The Geospatial Data Engineer will also develop visualization products and ensure appropriate knowledge management and documentation.

The Geospatial Data Engineer will work closely with DI’s Applied Science team, the Global Alliances team, the Country Engagement team, UNICEF’s Frontier Data Network, UNICEF Country Offices and other relevant teams and partners.

• Identification of relevant datasets
• Identify and review natural hazard, exposure, climate risk and forecasting datasets that can be meaningfully combined with DI’s school location and connectivity data and prioritize datasets for integration with DI’s datasets and Giga Spatial (an open-source geospatial Python library).
• Data processing and integration
  • Extend and apply Giga Spatial to process and integrate selected hazard datasets with other DI data, including maintaining technical documentation for the Giga Spatial extension for Git Hub.
• Country use case identification
  • Identify countries and priority use cases for hazard risk analysis in line with programmatic needs and collaborate with relevant UNICEF teams and partners to pilot country level analyses and contribute to the documentation of use cases.
• Piloting use cases for school-level hazard risk and connectivity analysis
  • Pilot school-level hazard risk and connectivity analyses by integrating real-time and event-based hazard data with school location and connectivity datasets, in collaboration with relevant UNICEF teams.
• Visualization and guidance products
  • Explore ways to integrate the produced data analysis outputs in UNICEF’s visualization tools to support interpretation and use of results.
• Documentation and knowledge management
  • Develop and maintain appropriate documentation and knowledge management resources to support the work for future use.

• Identification of relevant datasets
  • Detailed list of reviewed datasets (including source, spatial resolution, temporal coverage, update frequency and access) with recommendations on suitability for integration with DI data, e.g. in Excel format.
  • Structured inventory of prioritized datasets, e.g. in Excel format.
  • Access to prioritized datasets secured.
• Data processing and integration
  • Giga Spatial extended to handle the new datasets.
  • Datasets preprocessed.
  • Giga Spatial extension documented for Git Hub.
• Country use case identification
  • Summary of country-specific use cases, analytical questions, and confirmed country priorities.
• Piloting use cases for school-level hazard risk and connectivity analysis
  • At least two use cases of integrating hazard data with DI’s data successfully piloted in selected emergencies.
  • Web app developed to visualize findings.
  • Brief analysis report with the findings of each use case pilot (e.g. affected schools, impact on connectivity and continuity of education), highlighting practical applications.
  • Brief report including methodology, limitations, country use cases and lessons learned from the pilots.
• Visualization and guidance products
  • Integration with UNICEF visualization tools or brief report on integration possibilities.
  • User guide or training materials.
• Documentation and knowledge management
  • Established and regularly updated knowledge repository.
  • Brief report on progress, recommendations and lessons learned.
  • Academic report on findings of pilots.

• An Advanced University Degree (Master’s degree or equivalent) preferably in geospatial analysis, spatial data science or a related field is required.
• A first level University Degree (Bachelor’s degree of…