Top Data Engineer Jobs

The Data Annotator will label and curate data for AI model training, including annotating multimodal data and generating multiple-choice questions. The role requires attention to detail, English proficiency, and data literacy.

The Automation Data Analytics engineer at Bristol Myers Squibb is responsible for providing data engineering and analytics solutions for biologics manufacturing. Key responsibilities include system administration support, development of analytics tools, troubleshooting incidents, and training users on automation platforms.

The Senior Data Engineer will lead the design and development of high-performance data architectures for batch and streaming processing, manage ETL/ELT processes, optimize database systems, drive continuous improvement in data workflows, and collaborate with teams to support data-driven decision-making.

The Analytics Engineer at Zepz will develop and maintain data models, optimize data pipelines, and collaborate with cross-functional teams to provide actionable insights and ensure data quality. The role involves troubleshooting data issues and adhering to best practices for data consumption.

The Data Engineering Lead is responsible for developing scalable data and analytics solutions, implementing high-quality codebases, and collaborating with various teams to drive product innovation. They will also stay updated with industry trends, act as a subject matter expert, and facilitate a culture of innovation within the team.

As a Database Administrator, you will design, install, configure, and maintain database systems while ensuring their integrity and security. Your role involves troubleshooting, implementing performance tuning, managing backups, disaster recovery processes, and collaborating with technology teams. You'll also support Azure SQL instances and develop future compliance roadmaps.

As a Lead Data Engineer, you will design and architect cloud data lakes, implement tabular data formats, develop scalable data platforms, and collaborate with AI teams to enable data pipelines for model training and inference while ensuring data quality and governance.