Databricks is looking for a Sr Specialist Solutions Architect (SSA) - Data Engineering to guide customers in building big data solutions on Databricks, aligning their technical roadmap for expanding the usage of the Databricks Data Intelligence Platform.
Requirements
- 5+ years experience in a technical role with expertise in at least one of the following: Software Engineering/Data Engineering: data ingestion, streaming technologies - such as Spark Streaming and Kafka, performance tuning, troubleshooting, and debugging Spark or other big data solutions
- Extensive experience building big data pipelines
- 5 years professional experience with Big Data technologies (Ex: Spark, Hadoop, Kafka) and architectures
- Experience maintaining and extending production data systems to evolve with complex needs
- Experience scaling big data workloads (such as ETL) that are performant and cost-effective
- Experience migrating Hadoop workloads to the public cloud - AWS, Azure, or GCP
- Production programming experience in SQL and Python, Scala, or Java
Responsibilities
- Provide technical leadership to guide strategic customers to successful implementations on big data projects, ranging from architectural design to data engineering to model deployment
- Architect production level data pipelines, including end-to-end pipeline load performance testing and optimization
- Become a technical expert in an area such as data lake technology, big data streaming, or big data ingestion and workflows
- Assist Solution Architects with more advanced aspects of the technical sale including custom proof of concept content, estimating workload sizing, and custom architectures
- Provide tutorials and training to improve community adoption (including hackathons and conference presentations)
- Contribute to the Databricks Community
Other
- 5 years customer-facing experience in a pre-sales or post-sales role in healthcare industry
- Can meet expectations for technical training and role-specific outcomes within 6 months of hire
- Can travel up to 30% when needed
