Data Engineering IT Professional needs 5 years experience Data Engineering IT Professional requires: Gitlab version control and CI/CD Expertise in design and development of complex data pipelines Solutions using any industry leading ETL tools such as SAP Business Objects Data Services (BODS), Informatica Cloud Data Integration Services (IICS), IBM Data Stage. Experience of ELT tools such as DBT, Fivetran, and AWS Glue Expert in SQL - development experience in at least one scripting language (Python etc.), adept in tracing and resolving data integrity issues. Strong knowledge of data architecture, data design patterns, modeling, and cloud data solutions (Snowflake, AWS Redshift, Google BigQuery). Data Model: Expertise in Logical and Physical Data Model using Relational or Dimensional Modeling practices, high volume ETL/ELT processes. Performance tuning of data pipelines and DB Objects to deliver optimal performance. SAS ETL skills for current SAS script maintenance and minor enhancements, but should re ready to also use other ETL tools listed below for future state Experience in designing and building Data Warehouse and Data lakes. Good knowledge of data warehouse principles, and concepts. Technical expertise working in large scale Data Warehousing applications and databases such as Oracle, Netezza, Teradata, and SQL Server. Experience with public cloud-based data platforms especially Snowflake and AWS. Data Engineering IT Professional duties: Design, build, and maintain data pipelines that supports data integrations for Enterprise Data Warehouse, Operational Data Store or Data Marts etc