Solutions Architect - Public Sector
The Solutions Architect advises sales teams and customers on Databricks data and AI architectures, develops proofs of concept, and validates cloud integrations. The role requires 8+ years of experience in data engineering, data science, technical architecture, or pre-sales consulting, plus strong big data and cloud expertise.
About the job
Responsibilities
- Partner with the sales team and provide technical leadership to help customers understand how Databricks can solve their business problems.
- Develop account strategies and execute them with sales teams to help customers and prospects address business challenges.
- Consult on big data architectures and implement proofs of concept spanning data engineering, data science, machine learning, and SQL analysis workflows.
- Validate integrations with cloud services, internal tools, and third-party applications.
- Collaborate with other Solutions Architects to support customers and users.
- Promote Databricks open-source projects, including Apache Spark, Delta Lake, and MLflow, and recruit contributors from the developer community.
- Guide customers through competitive landscapes, best practices, and implementation approaches.
- Work with product and engineering teams to influence the product roadmap.
- Travel to customers in the region as needed.
Requirements
- 8+ years of experience in data engineering, data science, technical architecture, or a similar pre-sales or consulting role.
- 8+ years of experience with big data technologies, including Apache Spark, AI, data science, data engineering, Hadoop, and Cassandra.
- Strong consulting and customer-facing experience with external clients across multiple industries.
- Coding experience in Python, R, Java, Apache Spark, or Scala.
- Experience building distributed data systems.
- Experience building solutions with public cloud providers such as AWS, Azure, or Google Cloud.
Nice to Have
- Databricks Certification
Skills
Spark, Python, R, Java, Scala, Hadoop, Cassandra, Delta Lake, MLflow, AWS, Microsoft Azure, GCP, Machine Learning, SQL, Distributed Systems
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