Top Data Engineer Jobs

The Data Migration Consultant will execute data migrations, analyzing existing structures, ensuring data quality, and collaborating on migration plans. They will document processes and aid users in the post-migration phase while resolving issues as they arise.

The Senior Data Developer will develop and maintain data solutions using Python and Azure Databricks, focusing on high-quality coding practices. Responsibilities include managing data infrastructure, cloud services, and troubleshooting within SCRUM/Kanban workflows while applying a DevOps mindset.

The Senior Principal Data Engineer at Northrop Grumman will design, develop, and deploy scalable data architecture solutions while maintaining databases. Responsibilities include data mapping, ETL processes, query development, and collaborating with stakeholders to meet needs and objectives.

The Systems Engineering Analyst will support the design, development, and maintenance of applications, working with various programming languages and tools. Responsibilities include assisting systems engineers, developing documentation, implementing security controls, conducting testing, and managing multiple projects in a dynamic environment.

The role involves managing company-wide business planning processes, HR tech stack, and people data analytics workflows. Responsibilities include developing HR policies, supporting project management, and automating processes. The candidate will need to thrive in a fast-paced environment with a focus on collaboration and communication.

As a Microsoft Fabric or Azure BI Developer, you'll design, develop, and maintain Business Intelligence solutions, focusing on data architecture and delivery of insights. Responsibilities include implementing BI solutions, creating ETL processes, collaborating with stakeholders, and mentoring junior members.

The Senior Data Engineer will design, develop, and maintain data infrastructure, focusing on data warehouse solutions, ETL processes, and data governance practices. Responsibilities include optimizing data pipelines, implementing data quality initiatives, and modernizing data warehouse operations while collaborating with analysts and scientists to ensure data integrity and compliance.