Job Title | |
Location | : Houston, Texas (Hybrid) |
Type | : Contract |
Note: Candidate must have GCP with Geospatial Data Specialist
Job Summary
We are seeking a skilled GCP Data Engineer with strong expertise in geospatial data processing to join our growing data platform team. You will be responsible for designing, building, and optimizing scalable data pipelines on Google Cloud Platform (GCP), with a specific focus on ingesting, transforming, and analyzing geospatial datasets. Your work will directly support business decisions, advanced analytics, and AI/ML models.
Key Responsibilities
- Design and develop ETL/ELT pipelines on GCP using tools like Cloud Dataflow, Dataproc, BigQuery, Pub/Sub, and Cloud Composer.
- Integrate and process large-scale geospatial data (e.g., satellite imagery, GPS traces, shapefiles, raster/tile data).
- Use BigQuery GIS and other spatial analytics tools to develop location-based insights and spatial queries.
- Implement data modeling, partitioning, and performance tuning for high-volume geospatial datasets.
- Build and manage data lakes and warehouses for structured and unstructured geospatial data.
- Collaborate with data scientists, GIS analysts, and product teams to deliver geospatial data products and APIs.
- Ensure data quality, lineage, and governance using tools like Data Catalog, Looker, and GCP-native logging/monitoring.
Mandatory Skills
- 3+ years of hands-on experience as a Data Engineer on Google Cloud Platform (GCP).
- Strong proficiency with SQL (especially BigQuery GIS) and Python or Java.
- Experience handling geospatial data formats (GeoJSON, KML, shapefiles, raster, tiles) and spatial indexing techniques.
- Familiarity with PostGIS, GDAL, GeoPandas, or other GIS libraries and tools.
- Experience with streaming and batch data processing (Kafka/PubSub, Apache Beam, Dataflow).
- Solid understanding of data warehousing and data lake architectures.
- Experience with Airflow or Cloud Composer for orchestration.
- Background in GIS, remote sensing, mapping, or spatial data science.
- Experience building APIs or microservices for geospatial data consumption.
- Familiarity with machine learning pipelines involving geospatial features.
- Exposure to Google Earth Engine, Google Maps Platform, or CartoDB is a plus.
