
Data Engineer
1 week ago
We are seeking highly skilled GIS Data Engineers with strong expertise in Python, PySpark, and geospatial data processing to support the integration and analysis of spatial datasets. The selected candidates will work on large-scale data engineering projects in a distributed environment, focusing on building pipelines and workflows that support geospatial analytics and visualization.
Responsibilities :
Develop and maintain scalable ETL pipelines using PySpark and Python for processing GIS and spatial datasets.
Work with large geospatial datasets from various sources (e. g., shapefiles, GeoJSON, raster formats, satellite data).
Integrate geospatial data with enterprise systems (e. g., PostgreSQL/PostGIS, Big Data platforms).
Optimize queries and data transformations for performance and accuracy.
Collaborate with data scientists, GIS analysts, and business users to deliver data solutions.
Implement best practices for data quality, versioning, and lineage in spatial data pipelines.
Troubleshoot data issues and support production data workflows.
Requirements :
Strong hands-on experience with PySpark (Apache Spark with Python).
Advanced Python programming skills, especially in data handling and automation.
Experience with geospatial libraries such as GeoPandas, Shapely, Fiona, Rasterio, or GDAL.
Proficient in working with GIS formats (shapefiles, raster data, GeoTIFFs, KML, etc.)
Knowledge of spatial databases like PostGIS or GeoServer.
Hands-on experience with data lakes, big data processing, and cloud platforms
(Azure/AWS/GCP - preferred).
Strong understanding of data structures, algorithms, and spatial indexing.
Familiarity with MapReduce, Hive, or Databricks.
Prior experience in geospatial analytics, remote sensing, or urban planning domains.
Knowledge of DevOps, CI/CD pipelines, and containerization tools (Docker, Kubernetes).
Excellent communication and collaboration abilities.
Problem-solving mindset and attention to detail.
Ability to work independently in a client-facing, onsite role.
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