Top Entry Level Data Engineer Jobs

As an Enterprise Transformation Data Lead, you will enable digital transformation through data strategy, analysis, and migration to the Salesforce ecosystem. You will implement data migration strategies, analyze and map data, ensure quality and security, and collaborate with cross-functional teams to support data-driven initiatives.

As a Data Engineer at P&G, you will work on cloud native Big Data solutions, collaborating with a multi-disciplinary team to solve data challenges at scale. Responsibilities include designing and developing innovative solutions, managing the development backlog, and writing efficient code to support P&G's digital advertising efforts.

The Data Science Graduate role involves analyzing and validating data sets, developing models, and applying data science methodologies to derive actionable insights. The graduate will assist in the enhancement of metadata and contribute to visualizing model insights while supporting analytics solutions.

The Assistant Professor will teach undergraduate courses in Data Science and Mathematics, engage in research activities with students, and participate actively in departmental and university initiatives.

The Data I/O Specialist is responsible for processing and managing digital and physical assets, ensuring data integrity and facilitating communication across departments. Duties include downloading, ingesting, archiving files, verifying file integrity, managing metadata, and updating asset management systems, all while maintaining exceptional service and following security protocols.

The Oracle Database Administrator will provide on-call support, troubleshoot technical issues, and assist application and development teams. Responsibilities include deploying databases, testing software patches, implementing automation, monitoring database health, and handling backup and restores using Oracle RMAN.

The Big Data Engineer is responsible for designing and engineering data solutions, including coding data ingestion pipelines and maintaining data products. They collaborate with cross-functional teams, ensure data quality, and uphold data governance standards, while leveraging various technologies to support operational and analytic needs.