Data Engineer 1 (5 Year Fixed Term)

As a member of the Business Intelligence team, the Data Engineer 1 engages with multiple departments throughout the organization to take their data and transform it into information that will enable them to make better decisions. As an entry-level professional, the Data Engineer 1 gathers business requirements and designs, creates, and supports the data warehouse. 5 year fixed term This individual works with divine guidance to provide or support technology that furthers the mission of the Church and reflects the eternal impact of the gospel. We value early, mid and late-career candidates and encourage all applicants with the posted skills and abilities to apply. Bachelor's degree in related field or equivalent professional experience. Six months of data warehouse experience. Basic understanding of data analysis and report design/development. Good verbal and written communication skills. Contribute to resolving complex problems. Familiarity with data engineering best practices. Dimensional data modeling skills. Ability to quickly learn new tools and technology. Familiar with the ETL process and developing ETL code. Ability to ingest and clean large sets of structured and unstructured data Familiar with DevOps/DataOps process. Familiar with technologies such as SQL and Python. Exposure to the algorithms used for regressions, clustering, classification, forecasting, and constructing graphs. This job operates in a professional office environment. To successfully perform the essential functions of the job there may be physical requirements which need to be met such as sitting for long periods of time and using computer monitors/equipment. Possess and utilize knowledge specific to the data field to complete significant assignments. Maintain an understanding of the supported business processes. Gather and document requirements for the data warehouse, cloud database solutions, and technologies. Recognize and report security issues and requests and implement improvements. Utilize Star Schema data models. Support ETL jobs, pulling data from various source systems and loading data into the data warehouse. Use semantic layer to support end-user self-service. Create dashboards and reports using Microsoft Power BI or Tableau. Create reports that highlight areas in need of performance improvement.