Spark Engineer

Mission

As a Spark Technical Solutions Engineer, you will provide a deep dive technical and

consulting related solutions for the challenging Spark/ML/AI/Delta/Streaming/Lakehouse

reported issues by our customers and resolve any challenges involving the Data bricks

unified analytics platform with your highly comprehensive technical and customer

communication skills. You will assist our customers in their Data bricks journey and

provide them with the guidance, knowledge, and expertise that they need to realize

value and achieve their strategic objectives using our products.

Responsibilities

• Performing initial level analysis and troubleshooting issues in Spark using
  
  

Spark UI metrics, DAG, Event Logs for various customer reported 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 data bricks 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.

• Participate in the Designated Solutions Engineer program and drive one or
  
  

two of strategic customer's day to day Spark and Cloud issues.

• 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.

• 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.

• Coordinate with Engineering and Backline Support teams to provide
  
  

assistance in identifying, reporting product defects.

• Participate in weekend and weekday on-call rotation and run escalations during
  
  

data bricks 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.

• Provide best practices guidance around Spark runtime performance and
  
  

usage of Spark core libraries and APIs for custom-built solutions developed

by Data bricks customers.

• Be a true proponent of customer advocacy.
• Contribute in the development of tools/automation initiatives.
• Provide front line support on the third party integrations with Data bricks
  
  

environment.

• 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.

• Achieving above and beyond expectations of the support organization KPIs.
• Strengthen your AWS/Azure and Data bricks platform expertise through
  
  

continuous learning and internal training programs.

Competencies

• Min 6 years of experience in designing, building, testing, and maintaining
  
  

Python/Java/Scala based applications in typical project delivery and

consulting environments.

• 3 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 and hands-on experience with any SQL-based databases, Data
  
  

Warehousing/ETL technologies like Informatica, DataStage, Oracle,

Teradata, SQL Server, MySQL and SCD type use cases is preferred.

• Hands-on experience with AWS or Azure or GCP is preferred
• Excellent written and oral communication skills
• Linux/Unix administration skills is a plus
• Working knowledge in Data Lakes and preferably on the SCD types use
  
  

cases at production scale.

• Demonstrated analytical and problem-solving skills, particularly those that apply
  
  

to a Distributed Big Data Computing environment.