[Hiring] GIS Specialist @Telekom HBS

Role Description

• Review project requirements, client documentation, source datasets, reference geographies, and existing geospatial platform capabilities to define the geospatial implementation baseline.
• Assess geospatial data readiness, including raster datasets, vector layers, administrative boundaries, sub-national datasets, coordinate systems, spatial metadata, and known data quality constraints.
• Design and implement geospatial data preparation workflows, including:
  • Geometry validation
  • Coordinate reference system handling
  • Reprojection
  • Format normalization
  • Clipping
  • Joining
  • Agglomeration
  • Simplification
  • Enrichment
  • Spatial quality checks
• Support raster and vector data processing requirements, including:
  • Raster preparation
  • Vector layer management
  • Spatial indexing
  • Tiling considerations
  • Preparation of datasets for downstream analytics, APIs, map services, or visualization layers
• Participate in technical discussions with client stakeholders to clarify geospatial requirements, confirm assumptions, assess feasibility, explain limitations, and support acceptance of delivered outputs.
• Support the analysis, design, implementation, validation, and documentation of geospatial capabilities for the current project and future related initiatives.
• Implement or configure agreed geoprocessing functions, which may include:
  • Buffering
  • Proximity analysis
  • Spatial aggregation
  • Pattern analysis
  • Classification
  • Composite indexing
  • Clipping
  • Spatial joins
  • Sub-national processing
• Support the preparation and publication of geospatial outputs through agreed mechanisms, such as:
  • GeoJSON
  • Shapefile
  • CSV with coordinates
  • GeoTIFF where applicable
  • Map services
  • OGC aligned service patterns
  • APIs
  • Export packages
• Provide technical input to API and interoperability teams on geospatial service requirements, including:
  • OGC WMS
  • WFS
  • WCS
  • OGC API patterns
  • GeoJSON
  • Metadata alignment
  • Geospatial API payload structures where applicable
• Work with data engineering and data architecture teams to ensure geospatial datasets are correctly integrated into ingestion, transformation, validation, metadata, governance, and publication workflows.
• Support geospatial metadata, provenance, quality, and lineage requirements in coordination with data governance and catalogue teams.
• Define and execute geospatial validation activities, including:
  • Sample maps
  • Spatial accuracy checks
  • Comparison against reference layers
  • Export validation
  • Format validation
  • Documentation of known limitations
• Advise on geospatial performance considerations, including:
  • Map loading
  • Spatial query patterns
  • Export size
  • Raster processing complexity
  • Tile generation
  • Service consumption patterns
• Prepare clear technical documentation, configuration notes, supported format descriptions, workflow diagrams, validation evidence, operational considerations, and handover materials for technical teams.

Expected Deliverables

• Geospatial requirements and scope clarification notes, including assumptions, dependencies, constraints, and open questions.
• Assessment of geospatial data readiness, covering available raster datasets, vector layers, boundary datasets, coordinate systems, spatial metadata, data quality issues, and preparation needs.
• Geospatial implementation plan or work package description for agreed priority use cases.
• Configured or developed geospatial data preparation and processing workflows for the confirmed scope.
• Implemented geoprocessing outputs for agreed use cases, such as:
  • Agglomeration
  • Proximity analysis
  • Classification
  • Composite indexing
  • Clipping
  • Sub-national processing
• Validated geospatial outputs in agreed formats, such as:
  • GeoJSON
  • Shapefile
  • CSV with coordinates
  • GeoTIFF where applicable
  • Map service layers
  • API-ready spatial data products
• Technical documentation and handover package, including:
  • Workflow descriptions
  • Configuration notes
  • Supported formats
  • Operating guidance
  • Known limitations
  • Recommendations for future improvements

Qualifications

• Advanced university degree in Geographic Information Systems, Geography, Geomatics, Geospatial Science, Environmental Science, Computer Science, Data Science, Engineering, or a related field. A first-level university degree with additional relevant experience may be accepted.
• Proven professional experience in GIS, geospatial data engineering, spatial analytics, or geospatial solution implementation.
• Hands-on experience with raster and vector datasets, administrative boundaries, sub-national data, coordinate reference systems, spatial metadata, and geospatial data quality controls.
• Experience designing and implementing geospatial workflows such as data preparation, reprojection, geometry validation, spatial joins, aggregation, clipping, proximity analysis, buffering, raster processing, or map layer publication.
• Experience with common GIS formats and standards, including:
  • GeoJSON
  • Shapefile
  • KML/KMZ
  • CSV with coordinates
  • GeoTIFF
  • Web mapping or geospatial service patterns such as WMS, WFS, WCS, OGC API Features, and OGC API Tiles.
• Required Technical Skills:
  • GIS desktop, server, and/or open-source geospatial tools such as ArcGIS, QGIS, GDAL/OGR, GeoPandas, PostGIS, GeoServer, MapServer, or equivalent technologies.
  • Python for geospatial processing, including libraries such as GeoPandas, Rasterio, Shapely, Fiona, PyProj, Xarray, or similar.
  • SQL and spatial SQL, especially with PostGIS, SQL Server spatial, or equivalent spatial database capabilities.
  • Cloud-based geospatial processing experience, particularly in Azure, AWS, or Google Cloud environments.
  • Experience with data lake or lakehouse architectures and geospatial data integration in analytics platforms.
  • Familiarity with metadata standards relevant to geospatial and open data, such as ISO 19115, DCAT/DCAT-AP, Dublin Core, or equivalent.
  • Understanding of SDMX, environmental indicators, statistical/geospatial integration, or international reporting frameworks is an advantage.
• Required Soft Skills / Competencies:
  • Strong problem-solving skills and ability to assess technical feasibility, complexity, risks, and dependencies.
  • Ability to communicate geospatial concepts clearly to technical and non-technical stakeholders.
  • Strong attention to data quality, reproducibility, documentation, and operational maintainability.
  • Ability to work collaboratively with data engineers, data architects, API developers, governance specialists, project managers, and client stakeholders.
  • Ability to work independently, manage priorities, and deliver outputs within agreed timelines.
  • Awareness of data governance, responsible data management, interoperability, and open data principles.
  • Excellent command of written and spoken English.

Desirable Certifications

• Azure Data Engineering related certifications
• Azure Data Scientist Associate