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• PG :- - Not Required

• No of position :- ( 1 )

• Post :- 15th Sep 2022

DATA ARCHITECT INCLUDING DATA VAULT & WHERESCAPE. (MUST HAVE )

14 - 16 years of Data & Analytics experience with a focus on data architecture and data modeling
Be responsible for the development of the conceptual, logical, and physical data models - implementation of Operational Data Store (ODS), data warehouses, data marts, and data lakes on cloud platforms such as AWS and Snowflake
Practical knowledge in dimensional and Data Vault modeling (ERD, logical, physical), DDL scripting
Must have played a role of data modeler to create Data Vault-structured Warehouse and Star-Schema based data marts.
Provided the direction to ETL developers data movement from source systems to data vault model and data vault to data mart.
Exposure to WhereScape to create raw data vault tables for data vault model, data lineage, define data load approach
Oversee and govern the expansion of existing data architecture and the optimization of data query performance via best practices
Work with business and application/solution teams to implement data strategies, build data flows, and develop data models to meet business requirements and information needs
Engages in business requirement discussions and analyzes the source systems to gain an understanding of the data sources for the data platform
Define and govern data modeling and design standards, tools, best practices, and related development for enterprise data models.
Strong hands-on experience working with data modeling tools such as Erwin
Good technical understanding and appreciation for cloud data platforms such as AWS and Snowflake
Perform data analysis and profiling to gain an appreciation for the data
Work proactively and independently to address project requirements and articulate issues/challenges to reduce project delivery risks
Works and collaborates with Technical leads covering data integration, BI and analytics
Works with the DBA team to transform the logical data model into physical data model to be implemented in the target database
Develop transparent and well-understood enterprise data governance processes, rules, and technologies to support them, identify and promote best practices for data modeling within information areas
Strong written and verbal communication skills