Specialist Solutions Architect- Data Engineering & Warehousing

Details

• 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.
• 5+ years of experience in a technical role with deep expertise across the following areas: 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 Warehousing: Advanced query tuning, troubleshooting, data governance, and debugging MPP data warehouses or big data solutions. Experience migrating workloads from EDW systems (e.g., traditional SQL, Redshift, Snowflake, Synapse, EMR) across OLAP & OLTP workloads. Data Observability: Experience with SIEM tools (e.g., Splunk, Elastic, Sentinel), telemetry/high-velocity log ingestion, and anomaly detection.
• 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 Warehousing: Advanced query tuning, troubleshooting, data governance, and debugging MPP data warehouses or big data solutions. Experience migrating workloads from EDW systems (e.g., traditional SQL, Redshift, Snowflake, Synapse, EMR) across OLAP & OLTP workloads.
• 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.
• 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.
• Degree or Equivalent: 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.
• 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 Warehousing: Advanced query tuning, troubleshooting, data governance, and debugging MPP data warehouses or big data solutions. Experience migrating workloads from EDW systems (e.g., traditional SQL, Redshift, Snowflake, Synapse, EMR) across OLAP & OLTP workloads.
• Data Observability: Experience with SIEM tools (e.g., Splunk, Elastic, Sentinel), telemetry/high-velocity log ingestion, and anomaly detection.
• 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.