Description


  • Data Modeling: Knowledge of star and snowflake schemas, and the ability to design optimized data structures for analytical processing.
  • SQL Proficiency: Advanced SQL abilities for complex querying, along with experience in database programming for Redshift. Leverage Redshift Provisioned for query/workload isolation.
  • Performance Optimization: Skills in query tuning and understanding Redshift's performance features, like sort keys and distribution strategies.
  • AWS Ecosystem Knowledge: Familiarity with AWS services integral to data warehousing, like S3, AWS Glue, and IAM for security.
  • Analytical Problem-Solving: The capacity to solve complex data problems, optimize data warehouse solutions, and effectively analyze requirements.
  • Additionally, skills for building a re-usable enterprise assets and capabilities for persisting, processing, and analyzing data across the enterprise and minimizing “data silo.”
  • Build up to three (3) zones (Raw, Conformed, and Published), and new technological solutions, such as Redshift, Redshift Spectrum, Power BI, and AWS Data Migration were applicable.
  • Understanding and hands on experience with building a three-zoned data architecture based on a combination of a Data Lake (AWS S3) and Data Warehouse:
  • Raw data is maintained as a record of what data is received from the source and can additionally be archived.
  • Conformed/Curated data is data that is minimally processed to address potential data quality issues and to standardized formats and values. The curated data is potentially used for ad hoc analysis and to be easily integrated into data products in the published zone.
  • Published zone: This zone houses data products which are meant to meet specific analytical business use cases, limited to 3 business use cases based for the initial phase.

Key Skills
Education

Any Graduate

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