Sr. Data Engineer

Job Category: Information Technology Job Title: Sr. Data Engineer Duties: Candidates based in SF Bay Area preferred, but 100% remote is okay as long as they can work PST hours. As a Data Engineer, you will be responsible for engineering data pipelines for Splunk's enterprise data platform, democratizing datasets, enabling advanced analytics capabilities, integrating data from various systems, and applications. You will work as part of an evolving Data Technologies Team to rapidly design, secure, build, test and release new data enablement capabilities. The role will collaborate closely with other specialists, InfoSec Consultants, Product Managers & Owners, and internal customers. Develop data processing frameworks (ETL/ELT) for Enterprise Data Warehouse. Build large-scale batch and real-time data pipelines using the cloud data technologies, such as Snowflake, Python, and Apache Airflow Serve as a resource for data technologies implementations partnering with other functional teams and collaborate with data owners, business owners, and leaders. Supports the design and development of framework based data integration and interoperability across multiple Splunk Business applications. Advanced knowledge of physical database design and data structures. Expert knowledge of one or more database management systems such as AWS Redshift, Snowflake. Expert level skills in SQL, Python based data integration, data modeling and data architecture Skills: 12 years of experience as a Data Warehouse Developer, Data Engineer. 5+ years of experience driving adoption and building automation of data technology services and tools. 5+ years of hands-on experience with Python API based ELT automation framework, or interface design, development and maintenance. Large scale design, implementation and operations of Cloud data storage technologies such as AWS Redshift and Snowflake. 5+ years technical knowledge of programming scripting and data science languages such as Python, R, SQL, etc. Experience in building data models, including conceptual, logical, and physical for Enterprise Relational, and Dimensional Databases. test