Sr. Solutions Architect - Public Sector (sled)

Databricks Databricks · Data AI · New York, NY · Field Engineering - FE Direct Regulated

Solutions Architect role focused on leading the adoption of the Databricks Unified Analytics Platform for public sector clients. Responsibilities include partnering with sales, providing technical leadership, consulting on big data architecture, implementing proof of concepts, building reference architectures, and promoting open-source projects. Requires experience in cloud platforms, data engineering, data analytics, and data science/ML, with a focus on customer-facing pre-sales or technical architecture roles.

What you'd actually do

  1. Partner with the sales team to help customers understand how Databricks can help solve their business problems
  2. Provide technical leadership for customers to evaluate and adopt Databricks
  3. 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 3rd party applications
  4. Build and present references architectures, how-tos, and demo applications for customers
  5. Become an expert in, and promote Databricks inspired open-source projects (Spark, Delta Lake, MLflow, and Koalas) across developer communities through meetups, conferences, and webinars

Skills

Required

  • 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)

Nice to have

  • Degree in a quantitative discipline (Computer Science, Applied Mathematics, Operations Research)

What the JD emphasized

  • customer-facing
  • technical leadership
  • data science and machine learning projects
  • public cloud providers
  • Data Engineering technologies
  • Data Warehousing