Q1 Technologies
Data Warehouse Architect
Q1 Technologies, Baltimore, Maryland, United States, 21276
ROLE: AWS data Architect
PRIMARY SKILLS:
Data warehousing , pyspark , Github, AWS data platform, Glue, EMR, RedShift, databricks,Data Marts. DBT/Glue/EMR or Matillion, Bigdata ecosystem (Hadoop), data architecture, data engineering, data modelling, data consumption. 10-15 years of total experience and at least 3+ years of expertise in Cloud data warehouse technologies on AWS data platform covering Glue, EMR, RedShift, databricks etc. At least one End-to-end AWS data platform implementation is a must covering all aspects including architecture, design, data engineering, data visualization and data governance (specifically data quality and lineage). Significant experience with data migrations and development of Operational Data Stores, Enterprise Data Warehouses, Data Lake and Data Marts. Good hands-on knowledge on SQL and Data Warehousing life cycle is an absolute requirement. Significant experience with data migrations and development, design, Operational Data Stores, Enterprise Data Warehouses and Data Marts. Experience with cloud ETL and ELT in one of the tools like DBT/Glue/EMR or Matillion or any other ELT tool and exposure to Bigdata ecosystem (Hadoop). Expertise with at least one of the Traditional data warehouses solutions on Oracle, Teradata, and Oracle Exadata. Excellent communication skills to liaise with Business & IT stakeholders. Expertise in planning execution of a project and efforts estimation. Understanding of Data Vault, data mesh and data fabric architecture patterns. Exposure to working in Agile ways of working.
Data warehousing , pyspark , Github, AWS data platform, Glue, EMR, RedShift, databricks,Data Marts. DBT/Glue/EMR or Matillion, Bigdata ecosystem (Hadoop), data architecture, data engineering, data modelling, data consumption. 10-15 years of total experience and at least 3+ years of expertise in Cloud data warehouse technologies on AWS data platform covering Glue, EMR, RedShift, databricks etc. At least one End-to-end AWS data platform implementation is a must covering all aspects including architecture, design, data engineering, data visualization and data governance (specifically data quality and lineage). Significant experience with data migrations and development of Operational Data Stores, Enterprise Data Warehouses, Data Lake and Data Marts. Good hands-on knowledge on SQL and Data Warehousing life cycle is an absolute requirement. Significant experience with data migrations and development, design, Operational Data Stores, Enterprise Data Warehouses and Data Marts. Experience with cloud ETL and ELT in one of the tools like DBT/Glue/EMR or Matillion or any other ELT tool and exposure to Bigdata ecosystem (Hadoop). Expertise with at least one of the Traditional data warehouses solutions on Oracle, Teradata, and Oracle Exadata. Excellent communication skills to liaise with Business & IT stakeholders. Expertise in planning execution of a project and efforts estimation. Understanding of Data Vault, data mesh and data fabric architecture patterns. Exposure to working in Agile ways of working.