Full Stack Engineer – 5569
We are looking for a Full Stack Engineer who will work closely with Data Scientists on data engineering, making sure projects flow from prototype to production. The Full Stack Engineer will report to the Project CTO and 80% of the person’s time will be spent on architecture-level and senior engineering problems. In the remaining 20% of his or her time, the person will manage issues and projects encountered by Data Scientists and coordinate how to attack roadblocks in the current development plan. The Full Stack Engineer will work closely with the Project CTO and Project COO to make sure projects stay on schedule. Occasionally, the Full Stack Engineer will be called upon to produce or contribute to projections as to project cost, project completion, and other metrics needed for key presentations, stakeholder relations, and so forth.
The Full Stack Engineer is expected to work with Data Scientists on data engineering and data architecture problems to build viable prototypes, refine existing prototypes, and to ensure projects make it to production at the level of performance and quality enterprise SaaS customers expect. The Full Stack Engineer should have strong programming ability in C/C++, Java, and/or Python and should possess deep experience working with stream processing. Fluency in the design, construction, and efficient use of databases to attack large data science problems is preferable and experience using APIs with cloud service platforms like AWS and/or gcloud is necessary. Experience harvesting data from the web and from geospatial/geocoordinate sources (GIS, proprietary mapping, GMaps cross-referenced databases, etc.) is a plus.
Experience working with imagery, machine vision, and image taxonomy applications for machine learning is highly desirable.
- C/C++, Java, and/or Python
- AWS and/or gcloud
- Experience working with stream processing
- Design, construction, and efficient use of databases to attack large data science problems
- Data harvesting from geospatial/geocoordinate sources (GIS, proprietary mapping, GMaps cross-referenced databases, etc.)
- Experience working with imagery, machine vision, and image taxonomy applications for machine learning