DatabricksRemote
Specialist Solutions Architect - Data Engineering & Observability
United States of America
Full Time
2 hours ago
$180,000 - $248,000 USD
H1B Sponsor Likely
Key skills
Data EngineeringData WarehousingData IngestionSpark StreamingKafkaPerformance TuningTroubleshooting SparkBig Data SolutionsRisk ModelingFraud DetectionCustomer Lifetime ValueSIEM ToolsSplunkElasticSentinelTelemetryLog IngestionAnomaly DetectionETLELTCloud EnvironmentsBatch IngestionChange Data CaptureStreaming IngestionAWSAzureGCPLakehouse SystemsWarehouse SystemsDelta LakeData ModelingData GovernanceBusiness Intelligence IntegrationSQLPythonScalaJavaAIData LakeAnalyticsBIDatabricksSparkHadoopLeadershipSales
About this role
Databricks is the data and AI company that helps organizations unify and democratize data, analytics, and AI. They are seeking a Specialist Solutions Architect - Data Engineering & Observability to guide customers through cloud data engineering and warehousing transformations, collaborating with Solutions Architects and providing technical leadership for successful implementations on big data projects.
Responsibilities:
- 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 warehousing
- Hands-on experience with data ingestion, streaming technologies (e.g., Spark Streaming, Kafka), performance tuning, troubleshooting, and debugging Spark or other big data solutions
- Experience building data-driven use cases, such as risk modeling, fraud detection, and customer lifetime value (LTV)
- 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
- Ability to meet expectations for technical training and role-specific milestones within 6 months of hire
- Willingness to travel up to 30% as needed
- Prior customer-facing experience in a pre-sales or post-sales technical role