Senior Technical Solutions Engineer

Was macht eigentlich ein Solution-Engineer?

The impact you will have:

Performing initial level analysis and troubleshooting issues in Spark using Spark UI metrics, DAG, Event Logs for various customer reportxed job slowness issues.
Troubleshoot, resolve and suggest deep code-level analysis of Spark to address customer issues related to Spark core internals, Spark SQL, Structured Streaming, Delta, Lakehouse and other databricks runtime features.
Assist the customers in setting up reproducible spark problems with solutions in the areas of Spark SQL, Delta, Memory Management, Performance tuning, Streaming, Data Science, Data Integration areas in Spark.
Contribute in the development of tools/automation initiatives.
Participate in the Designated Solutions Engineer program and drive one or two of strategic customer’s day to day Spark and Cloud issues.
Provide best practices guidance around Spark runtime performance and usage of Spark core libraries and APIs for custom-built solutions developed by Databricks customers.
Provide front line support on the third party integrations with Databricks environment.
Plan and 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 internal stakeholders and customers, helping in driving the major spark issues at an individual contributor level.
Review the Engineering JIRA tickets and proactively intimate the support leadership team for following up on the action items.
Manage the assigned spark cases on a daily basis and adhere to committed SLA’s.
Build an internal wiki, knowledge base with technical documentation, manuals for the support team and for the customers. Participate in the creation and maintenance of company documentation and knowledge base articles.
Achieving above and beyond expectations of the support organization KPIs.
Coordinate with Engineering and Backline Support teams to provide assistance in identifying, reporting product defects.
Be a true proponent of customer advocacy.
Participate in weekend and weekday on-call rotation and run escalations during databricks runtime outages, incident situations, ability to multitask and plan day 2 day activities and provide escalated level of support for critical customer operational issues, etc.
Strengthen your AWS/Azure and Databricks platform expertise through continuous learning and internal training programs.

What we look for:

Min 3+ years of experience in Designing, building, testing, and maintaining Python/Java/Scala based applications in a typical project delivery and consulting environments.
2 years of hands-on experience in developing any two or more of the Big Data, Hadoop, Spark,Machine Learning, Artificial Intelligence, Streaming, Kafka, Data Science, ElasticSearch related industry use cases at the production scale. Spark experience is mandatory.
Hands on experience in the performance tuning/troubleshooting of Hive and Spark based applications at production scale.
Proven and real time experience in JVM and Memory Management techniques such as Garbage collections, Heap/Thread Dump Analysis is preferred.
Working knowledge in Data Lakes and preferably on the SCD types use cases at production scale.
Working and hands-on experience with any SQL-based databases, Data Warehousing/ETL technologies like Informatica, DataStage, Oracle, Teradata, SQL Server, MySQL is preferred.
Linux/Unix administration skills is a plus
Hands-on experience with AWS or Azure or GCP is preferred
Excellent written and oral communication skills
Demonstrated analytical and problem-solving skills, particularly those that apply to a “Distributed Big Data Computing” environment.

Benefits