Sr. Solutions Architect - Public Sector (SLED)

FEQ426R760 While candidates in the listed location(s) are encouraged for this role, candidates in other locations will be considered in the PST/MST time zone Mission Solutions Architects at Databricks lead the growth of the Databricks Unified Analytics Platform. As a team, we have expertise in cloud platforms, data engineering, data analytics, and data science and machine learning. As a member of our team, you will exercise and develop expertise in those areas, using open-source projects such as Apache Spark, MLflow, and Delta Lake. This is a customer-facing role, where you will work with customers, your teammates, the product team, and our post-sales teams, to identify use cases for Databricks, develop architectures and solutions using our platform, and guide customers through the implementation, to accomplish value. In that process, you will build relationships with our customers, find and build champions, and become a trusted advisor. You will report to the Field Engineering Manager for the team. The impact you will have: Partner with the sales team to help customers understand how Databricks can help solve their business problems Provide technical leadership for customers to evaluate and adopt Databricks Consult on big data architecture, implement proof of concepts for strategic customer projects, data science and machine learning projects, and validate integrations [... source excerpt omitted ...] in, and promote Databricks inspired open-source projects (Spark, Delta Lake, MLflow, and Koalas) across developer communities through meetups, conferences, and webinars Travel to customers in your region What we look for: 8+ 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 Experience supporting Public Sector clients Have built solutions with public cloud providers such as AWS, Azure, or GCP Data Engineering technologies (Ex: Spark, Hadoop, Kafka) Data Warehousing (Ex: SQL, OLTP/OLAP/DSS)