Specialist Solutions Architect - Data Engineering & Observability

## Responsibilities - Guide customers through cloud data engineering transformations across a wide variety of use cases. - Collaborate with and support Solutions Architects, requiring hands-on production experience with large-scale data engineering technologies and lakehouse architecture. - Help customers navigate evaluations and successful production planning for their business intelligence workloads while aligning their technical roadmap with the Databricks Data Intelligence Platform. - 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. ## Requirements - 5+ years of experience in a technical role with deep expertise across data engineering and data observability. - 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. - Deep expertise across multiple core data engineering domains, including: - 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 managing modern lakehouse and warehouse systems, including Delta Lake technologies, data modeling, governance, and BI integration. - Production programming experience in SQL and at least one of the following: Python, Scala, or Java. - Strong familiarity with cloud infrastructure providers (AWS, Azure, or GCP) is highly desirable. - Bachelor's degree in Computer Science, Information Systems, Engineering, or equivalent professional experience. - [Preferred] Prior customer-facing experience in a pre-sales or post-sales technical role. - Ability to meet expectations for technical training and role-specific milestones within 6 months of hire. - Willingness to travel up to 30% as needed. ## Nice to Have - Prior customer-facing experience in a pre-sales or post-sales technical role. ## Benefits - Mentorship, continuous learning, and internal training programs. - Opportunity to establish oneself as a leader in the data engineering and warehousing specialty. - Contribution to the Databricks Community. - Participation in hackathons and conference presentations.