DATA ENGINEER(REMOTE SENSING/SATELLITE DATA)

Role : Data Engineering (Remote Sensing / Satellite Data)

Total Exp 8+ Years (Minimum)

Rel EXP 8+ Years

Notice 30 Days

Skill

GeoPandas, Rasterio, PostGIS, ArcGIS Pro, cloud geospatial services, Airflow, remote sensing,

LiDAR, or satellite imagery processing, geospatial machine learning

Location Hyderabad

Education Bachelor’s or Master from Computer background

level of Interviews Internal - 1, Client Interview - 1

SSC Document Should submit with OTSI

Role Overview · We are looking for a GIS Data Engineer to design and maintain robust data pipelines for

processing, analyzing, and storing geospatial data (vector and raster formats). The ideal

candidate will have expertise in handling spatial datasets, optimizing geospatial workflows,

and integrating GIS data with modern data platforms for analytics and applications.

Job Description

· Geospatial ETL Pipelines: Develop scalable ETL processes to ingest, transform, and load

vector (e.g., GeoJSON, Shapefiles) and raster (e.g., GeoTIFF, NetCDF) data using tools like

GDAL, PostGIS, GeoPandas, or cloud services (AWS S3, Google Earth Engine).

· Spatial Data Processing: Clean, standardize, and perform geospatial operations (e.g.,

clipping, reprojection, buffering) on datasets for downstream use in analytics, mapping, or

machine learning.

· Database & Storage Optimization: Store and index geospatial data efficiently in spatially

enabled databases (e.g., PostGIS, BigQuery GIS) or data lakes (Delta Lake, Parquet) with

partitioning for performance.

· API & Integration: Build APIs or services to expose geospatial data (e.g., via GeoServer,

Mapbox, or ArcGIS Enterprise) and integrate with web/mobile applications or BI tools (e.g.,

Tableau, Power BI).

· Performance at Scale: Optimize queries and processing for large-scale raster/vector

datasets using distributed computing (e.g., Apache Sedona, Spark with GeoMesa) or parallel

processing techniques.

· Quality & Governance: Implement data validation checks for spatial accuracy, metadata

management, and compliance with standards (e.g., ISO 19115, OGC standards).

Skill Set

· Proficiency in Python (GeoPandas, Rasterio), SQL (PostGIS), and/or Java/Scala for

geospatial libraries.

· Experience with GIS tools (QGIS, ArcGIS Pro), cloud geospatial services (AWS Location,

Google Maps APIs), and workflow orchestration (Airflow, FME).

· Knowledge of remote sensing, LiDAR, or satellite imagery processing (e.g., NDVI, pixel

analysis) is a plus.

· Familiarity with geospatial machine learning (e.g., land cover classification) or 3D data

(e.g., Cesium, CityJSON).