SnowFlake developer

Technical Skills: Knowledge of SQL language and cloudbased technologies Data Warehousing concepts data modeling metadata management Data lakes multidimensional models data dictionaries Migration to AWS or Azure Snowflake platform Performance tuning and setting up resource monitors Snowflake modeling roles databases schemas SQL performance measuring query tuning and database tuning ETL Tools with clouddriven skills Integration with thirdparty tools Ability to build analytical solutions and models Coding in languages like Python Java Root cause analysis of models with solutions Hadoop Spark and other warehousing tools Managing sets of XML JSON and CSV from disparate sources SQLbased databases like Oracle SQL Server Teradata etc. Snowflake warehousing architecture processing administration Data ingestion into Snowflake Enterpriselevel technical exposure to Snowflake applicationsSoft Skills: Project management Problemsolving Innovation and best coding practices Interpersonal presentation and communication skills Critical and outofthebox thinking Analytical quantitative problemsolving and organizational skills Testing and test case preparation abilities Create test and implement enterpriselevel apps with Snowflake Design and implement features for identity and access management Create authorization frameworks for better access control Implement novel query optimization major security competencies with encryption Solve performance issues and scalability issues in the system Transaction management with distributed data processing algorithms Possess ownership right from start to finish Build monitor and optimize ETL and ELT processes with data models Migrate solutions from onpremises setup to cloudbased platforms Understand and implement the latest delivery approaches based on data architecture Project documentation and tracking based on understanding user requirements Perform data integration with thirdparty tools including architecting designing coding and testing phases Manage documentation of data models architecture and maintenance processes Continually review and audit data models for enhancement Maintenance of ideal data pipeline based on ETL tools Coordination with BI experts and analysts for customized data models and integration Code updates new code development and reverse engineering Performance tuning user acceptance training application support Maintain confidentiality of data Risk assessment management and mitigation plans Regular engagement with teams for status reporting and routine activities Migration activities from one database to another or onpremises to cloud