Databricks
Solutions Architect- Strategic AI Natives
Remote Field Engineering - FE Direct Enterprise role with clear candidate location fit.
PostedRecently added
Eligible countries1 accepted country
Seniority signalLead
Work settingRemote
Accepted candidate locations
USA
Role overview
Solutions Architect- Strategic AI Natives
Requirements and responsibilities
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Details
- You will partner with the sales team and provide technical leadership to help customers understand how Databricks can help solve their business problems.
- Consult on Big Data architectures, implement proof of concepts for strategic projects, spanning data engineering, data science and machine learning, and SQL analysis workflows. As well as validating integrations with cloud services, home grown tools, and other 3rd party applications
- Collaborate with your fellow Solutions Architects, using your skills to support each other and our users
- Become an expert in, promote, and recruit contributors for Databricks inspired open-source projects (Spark, Delta Lake, and MLflow) across the developer community.
- 5+ years in a data engineering, data science, technical architecture, or similar pre-sales/consulting role
- Experience building distributed data systems
- Comfortable programming in, and debugging, Python and SQL
- Have built solutions with public cloud providers such as AWS, Azure, or GCP
- Expertise in one of the following: Data Engineering technologies (Ex: Spark, Hadoop, Kafka) Data Science and Machine Learning technologies (Ex: pandas, scikit-learn, pytorch, Tensorflow)
- Data Engineering technologies (Ex: Spark, Hadoop, Kafka)
- Data Science and Machine Learning technologies (Ex: pandas, scikit-learn, pytorch, Tensorflow)
- Available to travel to customers in your region
- [Desired] Degree in a quantitative discipline (Computer Science, Applied Mathematics, Operations Research)
- Nice to have: Databricks Certification
- Data Engineering technologies (Ex: Spark, Hadoop, Kafka)
- Data Science and Machine Learning technologies (Ex: pandas, scikit-learn, pytorch, Tensorflow)
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