Snowflake SME

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