Databricks
Solutions Architect
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
Requirements and responsibilities
Readable role content extracted into sections for faster review.
Details
- Provide technical leadership for customers to evaluate and adopt Data and AI solutions from Databricks
- Consult on big data architecture, implement proof of concepts for strategic customer projects, data science and machine learning projects, and validate integrations with cloud services and other 3rd party applications
- Build and present reference architectures, technical guides, and demo applications for customers
- Provide escalated support for critical customer operational issues
- Become an expert in, and evangelize Databricks driven open-source projects (Apache Spark™, Delta Lake, MLflow, Koalas) across developer communities through meetups, conferences, and webinars
- Use your strengths to help your fellow SA's, and drive cross-functional relationships across the company
- Travel to customers up to 30%
- 4+ years in a customer-facing pre-sales, technical architecture, or consulting role
- Experience designing and architecting distributed data systems
- Comfortable programming in and debugging at least one of Python, Scala, Java, SQL, or R
- Have built solutions with public cloud providers, such as AWS, Azure, or GCP
- Experience in at least one of the following: Data Engineering technologies (e.g., Spark, Hadoop, Kafka) Data Warehousing (e.g., SQL, OLTP/OLAP/DSS) Data Science and Machine Learning technologies (e.g., pandas, scikit-learn, HPO)
- Data Engineering technologies (e.g., Spark, Hadoop, Kafka)
- Data Warehousing (e.g., SQL, OLTP/OLAP/DSS)
- Data Science and Machine Learning technologies (e.g., pandas, scikit-learn, HPO)
- [Preferred] Degree in a quantitative discipline (e.g., Computer Science, Applied Mathematics, Operations Research, etc.)
- Nice to have: Databricks Certification
- Data Engineering technologies (e.g., Spark, Hadoop, Kafka)
- Data Warehousing (e.g., SQL, OLTP/OLAP/DSS)
- Data Science and Machine Learning technologies (e.g., pandas, scikit-learn, HPO)
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