Staff Technical Solutions Engineer, Data & AINew

Databricks
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The opportunity

Note: this is a hybrid role and requires ~3 days in the office in São Paulo-SP.

What you'll do

  • Be directly responsible for leading and driving technical solutions for a: breadth of complex problems reported by Databricks customers, including escalated and critical scenarios.

  • Deep Dive into code-level analysis and systems architecture of customer: workloads to address issues related to Databricks products - including Spark core internals, Spark SQL, Delta, DLT, and Model Serving.

  • Serve as a customer support advisor: be able to diffuse escalations during incidents directly with customer stakeholders, quickly find mitigations and solutions for advanced use cases, and be strategic at helping prevent future customer issues.

  • Help make the Databricks products simpler to use and customer production: environments more stable, such as by influencing Engineering and Backline Support teams to identify areas for product improvements.

  • Be known as a deep technical expert and leader in the Databricks platform and: productionizing systems, and mentor others in those areas to spread your knowledge.

  • Minimum of 8 years of experience in designing, building, testing, and: maintaining data pipelines. Relevant past Spark experience is mandatory.

What they're looking for

  • Expertise in SQL databases or data warehouses (such as Oracle, Teradata, SQL: Server, MySQL) and ETL technologies (such as Informatica, DataStage, Talend, Fivetran).
  • years of hands-on experience in developing two or more Big Data technologies,: such as Spark & Hadoop, Lakehouse architecture - such as Delta, Data Ingestion, Data Streaming applications, or ML/AI applications for industry use cases.
  • Around 3 years of hands-on experience as a technical lead on a Data & AI: Engineering team, serving as the go-to person to resolve critical issues.
  • Prior Support or customer-facing experience is not required for this role,: but proven ability to influence cross-functionally is, such as with internal stakeholders or engineering teams.