Staff Designated Support Engineer

P-1553 As a Staff Designated Engineer and tech subject matter expert, you will partner closely with our Field and Engineering teams to deliver high-touch specialized support and tailored technical solutions for Databricks' largest and most strategic customers in the Digital Native Business (DNB) segment. In this customer-facing role, you will leverage your technical expertise in Apache Spark™ and other data technologies to triage and resolve complex product issues and unblock our customers’ most critical technical challenges. The Impact You Will Have Perform advanced Troubleshooting and Root Cause Analysis to resolve performance and reliability issues in Spark, SQL, Delta, Streaming, and Databricks runtime features using tools like Spark UI metrics, Mosaic AI Model Service, DAGs, and event logs. Discover requirements for continuous monitoring to detect early performance issues working with R&D and NOC teams to optimize the DNB customer environments. Build Rapid POCs, Test/Deploy/Monitor the solutions built by Databricks Engineering to address customer challenges and showcase advanced Spark/ML/AI runtime capabilities aligned with their business goals. Develop comprehensive playbooks and maintain a knowledge base of common issues and solutions for Spark, ML, and AI workflows. Train customer engineering and business teams on best practices in performance tuning, debugging, and effectively leveraging Databricks Features. Pilot new best practices processes/ programs, champion process improvements, and collaborate with cross-functional teams to enhance the customer experience. Advocate for customers in business review meetings and maintain close relationships as a trusted advisor and primary technical point of contact. Collaborate onsite with Field Engineering, Sales, and Product teams during customer engagements and technical presentations to provide rapid solutions to production-impacting issues, demonstrating deep technical expertise and building strong customer trust. What We Look For Technical Expertise in Big Data and Spark: 8–12 years of experience designing, building, and troubleshooting distributed computing applications, with 4+ years delivering production-scale Spark/ML/AI solutions using Python, Java, or Scala. Data Engineering Specialization: Hands-on expertise with Data Lakes, SQL-based databases, and Cloud-based Data Warehousing/ETL tools like Snowflake, Redshift, Bigquery, etc Advanced Tech Skills : Deep knowledge of Spark core internals, Delta/Iceberg, JVM optimization, and memory management, with additional proficiency in AI ecosystems like Machine Learning, Deep Learning, and Generative AI. Cloud and CI/CD Skills: Practical experience with AWS, Azure, or GCP, coupled with expertise in building and managing CI/CD pipelines, monitoring, and alerting systems. Customer-Facing Experience: 3–5 years in customer-facing roles such as Technical Account Manager or Solutions Architect, demonstrating strong communication, relationship-building, and problem-solving skills. Advanced Proactive Problem Solving Skills : Proven ability to anticipate, identify, and mitigate risks while planning solutions for production challenges. Effectively use sound business judgment, risk avoidance and subject matter expert resources to coordinate team efforts to solve problems. Collaboration and Leadership: Proven ability to work with cross-functional teams and senior leadership to address roadblocks, mitigate risks, and drive customer success while creating impactful documentation for self-service solutions. About Databricks Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow.