Varite
Senior Data Engineer
Varite, Los Angeles, California, United States, 90079
Onsite Job : Los Angeles, CA
Pay Range : $60 - $65
Skills Preferred
Cloud Platforms:
Deep understanding of Azure ecosystem, including Azure Data Factory, Data Lake Storage, Blob Storage, power apps, and Functions. dditionally, in-depth understanding and implementation of API management such as Apigee. Big Data Technologies:
Proficiency in Databricks, Spark, PySpark, Scala, and SQL. Data Engineering Fundamentals:
Expertise in ETL/ELT processes, data pipelines, data modeling, schema design, and data warehousing. Programming Languages:
Strong Python and SQL skills, with knowledge of other languages like Scala or R beneficial. Data Warehousing and Business Intelligence:
Strong ERD concepts, designs, and patterns, Understanding of OLAP/OLTP systems, performance tuning, Database Server concepts, and BI tools (Power BI, Tableau). Data Governance:
Strong understanding of RBAC/ABAC, Data Lineage, Data leak prevention, Data security, and compliance. Deep understanding and implementation knowledge of audit and monitoring in Cloud.
Experience Preferred
Seven (7) years of applying Enterprise Architecture principles, with at least five (5) years in a lead capacity. Five (5) years of hands-on experience with Azure Data Factory, Azure Databricks, API implementation and management solution, and managing Azure resources. Five (5) years of experience in the following: developing data models and pipelines using Python; working with Lakehouse platforms; GitHub CI/CD pipelines and infrastructure automation, Terraform scripting; and with data warehousing systems, OLAP/OLTP systems, and integration of BI tools.
Education Preferred
This classification requires the possession of a bachelor's degree in an IT-related or Engineering field.
Skills Preferred
Cloud Platforms:
Deep understanding of Azure ecosystem, including Azure Data Factory, Data Lake Storage, Blob Storage, power apps, and Functions. dditionally, in-depth understanding and implementation of API management such as Apigee. Big Data Technologies:
Proficiency in Databricks, Spark, PySpark, Scala, and SQL. Data Engineering Fundamentals:
Expertise in ETL/ELT processes, data pipelines, data modeling, schema design, and data warehousing. Programming Languages:
Strong Python and SQL skills, with knowledge of other languages like Scala or R beneficial. Data Warehousing and Business Intelligence:
Strong ERD concepts, designs, and patterns, Understanding of OLAP/OLTP systems, performance tuning, Database Server concepts, and BI tools (Power BI, Tableau). Data Governance:
Strong understanding of RBAC/ABAC, Data Lineage, Data leak prevention, Data security, and compliance. Deep understanding and implementation knowledge of audit and monitoring in Cloud.
Experience Preferred
Seven (7) years of applying Enterprise Architecture principles, with at least five (5) years in a lead capacity. Five (5) years of hands-on experience with Azure Data Factory, Azure Databricks, API implementation and management solution, and managing Azure resources. Five (5) years of experience in the following: developing data models and pipelines using Python; working with Lakehouse platforms; GitHub CI/CD pipelines and infrastructure automation, Terraform scripting; and with data warehousing systems, OLAP/OLTP systems, and integration of BI tools.
Education Preferred
This classification requires the possession of a bachelor's degree in an IT-related or Engineering field.