Role:
Snowflake SME

Location:
Baltimore, MD- Initial Remote

Duration: 6+ months

Rate:
$market All Inclusive

Job Description

Skills:
(All Skillsets at least 10 years)
Snowflake SME
Snowflake space
ETL and informatic nice to have.
Knowledge of SQL language and cloud-based technologies (SQL - 10 years)
Data warehousing concepts, data modeling, metadata management
Data lakes, multi-dimensional models, data dictionaries
Performance tuning and setting up resource monitors
Snowflake modeling - roles, databases, schemas
SQL performance measuring, query tuning, and database tuning
ETL tools with cloud-driven skills
Ability to build analytical solutions and models
Root cause analysis of models with solutions
Hadoop, Spark, and other warehousing tools
Managing sets of XML, JSON, and CSV from disparate sources
SQL-based databases like Oracle SQL Server, Teradata, etc.
Snowflake warehousing, architecture, processing, administration
Data ingestion into Snowflake
Enterprise-level technical exposure to Snowflake applications

Responsibilities:
Create, test, and implement enterprise-level apps with Snowflake
Design and implement features for identity and access management
Create authorization frameworks for better access control
Implement Client 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 on-premises setup to cloud-based 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 third-party 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 on-premises to cloud

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