Geospatial Data Engineer
Summary
Build and maintain data pipelines for geospatial, weather, and crop data to power DNEXT’s agricultural market intelligence platform.
We are DNEXT, commodity agriculture experts. We provide consultancy to a variety of firms involved from the production stage all the way to the consumers and across multiple geographies. Our market intelligence platform hosts a full scope of datasets related to agricultural commodities.
We serve clients from 22 countries with a diverse team of 60+ professionals.
Job Profile
We are looking to hire a geospatial data engineer. Your responsibilities will consist of building and maintaining data pipelines for environmental and remote sensing data, ensuring timely availability and robust delivery of critical data used by in-house crop analysts as well as DNext clients. You will join a team of commodity analysts, data scientists, data engineers, and analysts.
Responsibilities
- Build and maintain data pipelines for remote sensing, weather and crop production data
- Analyze available weather, satellite and crop production data sets and identify new data sources
- Provide guidance and mentoring regarding data engineering data engineering best practices across the business
Qualifications
- University degree in computer science, mathematics, physics or similar subject
- Industry experience in the building and deploying robust data pipelines
Necessary experience:
- Proficiency in Python & Python data processing, especially for geospatial data (pandas, xarray, rasterio, geopandas, xvec, Airflow/Dagster, etc.)
- Experience in cloud computing, preferably AWS
- Good familiarity with data engineering best practices and data pipeline deployment & monitoring
- Experience working with modern GIS tools, e.g. QGIS
- High-level written and verbal communication skills
- Ability for teamwork, and capacity to handle short timelines
Desired experience:
- Experience working with weather forecast and/or reanalysis data sets
- Experience working with remote sensing data (MODIS, VIIRS, Sentinel 2, etc.)
- Experience working with agricultural production data sets alongside relevant geospatial reference data