Must also have any experience with or knowledge of i designing, developing, and testing efficient geospatial pipelines that encompass datacube generation containing raster and vector data against large geospatial data stores applied to machine learning model generation and deployment; ii designing and developing packages, proceduresmethods and scripts using Python, SQLPLSQL, or Java. Proven experience in design and implementation of scalable and stress applications; iii using remote sensing tools such QGIS, ArcGIS etc. and preprocessing workflows such as atmospheric correction, telemetry correction; iv working with raster and vector geospatial datasets by applying GDAL, proj4, changing resolutions, CRS, projections, etc.; v working in a matrixhybrid matrixhybrid organization structure with crossfunctional leadership skills as well as troubleshooting and making quick decisions; vi working with waterfall or Agile methodologies using Rational rose suite, Atlassian suite or JIRA; vii Big Data development including Python, Spark, Scala, Parquet; viii Cloud; AWS, GCP; ix code versioning and dependency management systems such as GitHub, Git, SVN; and x design and implementation of RDBMSNoSQL systems such as Oracle, Postgres, MSSQL, MongodB etc.br br Experience can be concurrent.br br Note since there is no experience required in H.6, the Kellogg language is not required.
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