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As a Specialist Solutions Architect (SSA) - Data Engineering , you will guide customers through cloud data engineering transformations across a wide variety of use cases.
In this customer-facing role, you will collaborate with and support Solutions Architects. This requires hands-on production experience with large-scale data engineering technologies and lakehouse architecture. The SSA teams help customers navigate evaluations and successful production planning for their business intelligence workloads while aligning their technical roadmap with the Databricks Data Intelligence Platform.
As a deep go-to expert reporting to the Specialist Field Engineering Manager, you will continue to strengthen your technical skills through mentorship, continuous learning, and internal training programs. In this role, you will establish yourself as a leader in the data engineering and warehousing specialty.
The impact you will have:
- Provide technical leadership to guide strategic customers to successful implementations on big data projects and large-scale data warehousing workloads.
- Prove the value of the Databricks Intelligence Platform for customer workloads by architecting production workloads, including end-to-end pipeline load performance testing and optimization.
- 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.
What we look for:
- Software / Data Engineering: Hands-on experience with data ingestion, streaming technologies (e.g., Spark Streaming, Kafka), performance tuning, troubleshooting, and debugging Spark or other big data solutions.
- Data Applications Engineering: Experience building data-driven use cases, such as risk modeling, fraud detection, and customer lifetime value (LTV).
- Data Observability: Experience with SIEM tools (e.g., Splunk, Elastic, Sentinel), telemetry/high-velocity log ingestion, and anomaly detection.
- Proven track record of maintaining, scaling, and extending production data systems to evolve with complex business needs.
- Designing and scaling cost-efficient, high-performance data workloads (ETL/ELT, analytics) in cloud environments.
- Building and migrating large-scale data pipelines, including batch, CDC (Change Data Capture), and streaming ingestion.
- Migrating on-premises or Hadoop-based data systems to modern cloud platforms (AWS, Azure, GCP). Developing and m