20 tasks, each one witnessed by the sources that watched the job — and behind every one, a prompt you can use tonight.
You will split time between the office and the field. Mornings often mean checking pipelines: Amazon EC2 instances, Apache Kafka streams, and batch jobs in Apache Hadoop or Hive that ingest new sensor or satellite images.
Afternoons can be hands-on: compiling and formatting image data, running routines to remove vegetation artifacts, reviewing radar or aerial photos, and meeting with technicians or planners about project goals. Field days involve collecting GPS points, airborne or climatic data, and keeping task records.
Expect cloud and big-data tools: Amazon EC2 for processing, Amazon Redshift or DynamoDB for storing results, and Apache Kafka + Hadoop/Hive for moving and batch-processing image streams. Bash scripting commonly glues steps together.
Project tracking often uses Atlassian JIRA. If you develop new sensor analyses, you’ll code automated correction routines and likely run experiments on EC2 clusters before deploying to production.
Test automated routines on labeled validation sets: collect field survey or climatic ground truth and compare outputs. For example, run a vegetation-correction routine on known sites and measure error rates before full deployment.
Log every change, keep records in JIRA, and version data and code (use DynamoDB/Redshift snapshots or code repositories). If uncertainty is high, flag results for human review rather than auto-accepting them.
BLS reports about 22,300 employed in this SOC (19-2099.01). The median pay is $122,570 per year; the lowest tenth is $67,000, and the top tenth is $195,190. Cite: Bureau of Labor Statistics (BLS) 2025 data.
Pay varies by employer, location, and whether you do fieldwork, build new sensors, or work with cloud infrastructure like EC2 and Redshift.
Compared with a GIS technician, you spend more time developing analytical techniques and sensor systems, studying radar images, and writing automated correction routines rather than just making maps. You also collect airborne and climate field data.
Compared with a data scientist, you often need domain-specific skills: visual literacy for images, remote sensing techniques, and hands-on fieldwork. You’ll also work more with systems like EC2, Kafka, and Hadoop for large geospatial image streams.
Learn to organize and preprocess geospatial imagery and related metadata. That includes reading aerial or radar photos, compiling image data, and using Bash to run simple pipelines on an EC2 instance.
This skill ties into many tasks: correcting image artifacts, preparing data for Hadoop/Hive ingestion, keeping task records, and helping technicians in the field. It makes you useful within weeks on real projects.