Sr Data & AI Technical Solutions Engineer

Databricks Databricks · Data AI · Brazil · Support

This role focuses on supporting customers in debugging and maintaining stable production data pipelines and AI workflows on the Databricks platform. The engineer will provide initial analysis, troubleshooting, and resolution for data engineering and AI workloads, perform deep dives into code-level analysis, and contribute to product improvements.

What you'd actually do

  1. Be the first point of contact for customer production issues - provide initial analysis, troubleshooting, and resolution for data engineering and AI workloads.
  2. Deep Dive into code-level analysis of customer workloads to address issues related to Databricks products - including Spark core internals, Spark SQL, Delta, DLT, and Model Serving.
  3. Provide excellent customer support - be knowledgeable and empathetic in customer communications over email and video, act with a sense of urgency to find mitigations and solutions, diffuse escalations during incidents and be proactive at helping prevent future customer problems.
  4. Help make the Databricks products simpler to use and customer production environments more stable - such as through coordinating with Engineering and Backline Support teams to identify areas for product improvements.
  5. Develop expertise in productionizing systems in Databricks and share your knowledge by contributing to wikis and other technical documentation to be used internally or externally by customers and partners.

Skills

Required

  • Python/Java/Scala based applications
  • SQL databases or data warehouses
  • ETL technologies
  • Spark & Hadoop
  • Lakehouse architecture
  • Data Ingestion
  • Data Streaming applications
  • ML/AI applications
  • performance tuning and troubleshooting of Data and AI applications at production scale
  • query optimization
  • memory management
  • garbage collection
  • heap/thread dump analysis

Nice to have

  • public cloud (AWS or Azure or GCP)

What the JD emphasized

  • production data pipelines
  • AI workflows
  • production troubleshooting
  • production scale
  • Model Serving

Other signals

  • troubleshooting production AI workloads
  • performance tuning and troubleshooting of Data and AI applications at production scale
  • Model Serving