Top Hybrid Data Engineering Jobs in Atlanta, GA

Lead data engineering projects, develop and implement data pipelines and architecture strategies, manage data quality, and mentor team members at PwC.

The role involves leading data architecture initiatives, developing AI models, and collaborating with data scientists to optimize data solutions and drive strategic decision-making.

Lead the development and deployment of data solutions with Palantir Foundry, driving projects, mentoring teams, and engaging with clients to solve complex problems.

As a Senior Data Engineer, you will design and develop data pipelines, ensure data quality, and collaborate on advanced analytics projects within the Applied Research team at Cox Automotive.

Lead the design and implementation of enterprise-scale Knowledge Graphs and Semantic AI solutions, integrating graph intelligence and Generative AI. Manage cross-functional teams in data architecture and governance.

The Data Analytics Engineer will develop data models, perform data wrangling, and create dashboards to drive insights for business decisions at Bose Corporation.

The Data Engineer will design scalable data architectures, implement data pipelines, and collaborate with teams to enhance AI/ML solutions for industrial customers, ensuring data quality and efficient workflows.

The Associate Data Engineer will participate in a training program focusing on data solutions, helping enterprises with data projects post-training. Responsibilities include effective communication, teamwork, problem-solving, and resourcefulness.

As a Data Engineer, you will build and maintain data pipelines, ensure data reliability, collaborate with teams, and drive improvements in data platforms while supporting analytics and decision-making.

The Senior Data Engineer designs and optimizes modern data platforms, leads client engagement, develops data pipelines, and contributes to project planning.

The Global Indirect Tax Automation Manager will manage third-party tax systems and custom billing system implementations, focusing on requirements gathering, design, build, and testing.

Design and implement cloud-native Azure data architectures, data models, ELT processes, governance, and security. Collaborate with engineering, BI, and data science teams to optimize pipelines, ensure data quality, document designs, and support system stabilization for analytics and operational use cases.