Role: Spark / Data Engineer @Sunnyvale, CA (Onsite)
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kesavan recruiter
Nov 4, 2025, 2:18:32 PM (yesterday) Nov 4
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Hi
Contract
Role: Spark / Data Engineer
Location: Sunnyvale, CA (Onsite)
Must have:
Spark, Scala, Big Data
JD:
• Has good knowledge on Snowflake Architecture.
• Understanding Virtual Warehouses - multi-cluster warehouse, autoscaling
• Metadata and system objects - query history, grants to users, grants to roles, users
• Micro-partitions
• Table Clustering, Auto Reclustering
• Materialized Views and benefits
• Data Protection with Time Travel in Snowflake - extremely imp
• Analyzing Queries Using Query Profile - extremely important (Explain plan)
• Cache architecture
• Virtual Warehouse(VW)
• Named Stages
• Direct Loading
• SnowPipe, Data Sharing,Streams, JavaScript Procedures & Tasks
• Strong ability to design and develop workflows in Snowflake in at least one cloud technology (preferably, AWS)
• Apply Snowflake programming and ETL experience to write Snowflake SQL and maintain complex, internally developed Reporting system.
• Preferable knowledge in ETL Activities like data processing from multiple source systems.
• Extensive Knowledge on Query Performance tuning.
• Apply knowledge of BI tools.
• Manage time effectively. Accurately estimate effort for tasks and meet agreed-upon deadlines. Effectively juggle ad-hoc requests and longer-term projects.
Snowflake performance specialist
- Familiar withzero copy cloningand usingtime travelfeatures to clone table
- Familiar in understandingSnowflake query profileand what each step does andidentifying performance bottlenecks from query profile
- Understanding of when a table needs to be clustered
- Choosing the right cluster keyas a part of table design to help query optimization
- Working with materialized views andbenefits vs cost scenario
- How Snowflake micro partitions are maintained and what are the performance implications wrt micro partitions/ pruningetc
- Horizontal vs vertical scaling. When to do what.Concept of multi cluster warehouse and autoscaling
- Advanced SQL knowledge including window functions, recursive queriesand ability to understand and rewritecomplex SQLs as a part of performance optimization
• Understanding Virtual Warehouses - multi-cluster warehouse, autoscaling
• Metadata and system objects - query history, grants to users, grants to roles, users
• Micro-partitions
• Table Clustering, Auto Reclustering
• Materialized Views and benefits
• Data Protection with Time Travel in Snowflake - extremely imp
• Analyzing Queries Using Query Profile - extremely important (Explain plan)
• Cache architecture
• Virtual Warehouse(VW)
• Named Stages
• Direct Loading
• SnowPipe, Data Sharing,Streams, JavaScript Procedures & Tasks
• Strong ability to design and develop workflows in Snowflake in at least one cloud technology (preferably, AWS)
• Apply Snowflake programming and ETL experience to write Snowflake SQL and maintain complex, internally developed Reporting system.
• Preferable knowledge in ETL Activities like data processing from multiple source systems.
• Extensive Knowledge on Query Performance tuning.
• Apply knowledge of BI tools.
• Manage time effectively. Accurately estimate effort for tasks and meet agreed-upon deadlines. Effectively juggle ad-hoc requests and longer-term projects.
Snowflake performance specialist
- Familiar withzero copy cloningand usingtime travelfeatures to clone table
- Familiar in understandingSnowflake query profileand what each step does andidentifying performance bottlenecks from query profile
- Understanding of when a table needs to be clustered
- Choosing the right cluster keyas a part of table design to help query optimization
- Working with materialized views andbenefits vs cost scenario
- How Snowflake micro partitions are maintained and what are the performance implications wrt micro partitions/ pruningetc
- Horizontal vs vertical scaling. When to do what.Concept of multi cluster warehouse and autoscaling
- Advanced SQL knowledge including window functions, recursive queriesand ability to understand and rewritecomplex SQLs as a part of performance optimization
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