Performing initial level analysis and troubleshooting issues in Apache Spark™ using Apache Spark™ UI metrics, DAG, Event Logs for multiple customer-reported job slowness issues.
Troubleshoot, resolve, and suggest deep code-level analysis of Apache Spark™ to address customer issues related to Apache Spark™ core internals, Apache Spark™ SQL, Structured Streaming, Delta, Lakehouse, and other Databricks runtime features.
Assist the customers in setting up reproducible Apache Spark™ problems with solutions in the areas of Apache Spark™ SQL, Delta, Memory Management, Performance tuning, Streaming, Data Science, and Data Integration areas in Apache Spark™.
Participate in the Designated Solutions Engineer program and guide one or two of the strategic customers’ daily Apache Spark™ and Cloud issues.
Coordinate with Account Executives, Customer Success Engineers, and Resident Solution Architects for coordinating the customer issues and best practices guidelines.
Participate in screen sharing meetings, answering Slack channel conversations with our team members and customers, helping in driving the major Apache Spark™issues at an individual contributor level.
Build an internal wiki, a knowledge base with technical documentation, manuals for the support team, and for the customers. Help create company documentation and knowledge base articles.
Coordinate with Engineering and Backline Support teams to help report product defects.
Participate in weekend and weekday on-call rotation and run escalations during databricks runtime outages, incident situations, and plan day-to-day activities, and provide an escalated level of support for important customer operational issues.
Requirements & Skills:
3 years of hands-on experience developing any two or more of the Big Data, Hadoop, Apache Spark™, Machine Learning, Artificial Intelligence, Streaming, Kafka, Data Science, ElasticSearch related industry use cases at the production scale. Spark experience is mandatory.
Experience in the performance tuning/troubleshooting of Hive and Apache Spark™-based applications at production scale.
Real-time experience in JVM and Memory Management techniques such as Garbage collections, Heap/Thread Dump Analysis.
Experience with any SQL-based databases, Data Warehousing/ETL technologies like Informatica, DataStage, Oracle, Teradata, SQL Server, MySQL, and SCD type use cases.
Experience with AWS or Azure, or GCP
Written and spoken proficiency in both Korean and English
