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# Data Engineer

- **Company:** Robert Half
- **Location:** Atlanta, GA, United States
- **Experience:** Experienced
- **Contract:** Temporary contract
- **Skills:** Artificial Intelligence, Data Analysis, Apache HTTP Server, Microsoft Azure, Information Engineering, Data Governance, Data Infrastructure, Extract Transform Load (ETL), Data Structures, Data Systems, DevOps, Python (Programming Language), Machine Learning, Operational Databases, SQL Databases, Technical Data Management Systems, Unstructured Data, Microsoft Fabric, Pyspark, Data Management, Data Pipelines, Databricks
- **Published:** August 21, 2026
- **Apply:** https://www.atlantacareerpath.com/job.asp?id=3361157269&tx=JK7569FFJ&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

stakeholders to understand requirements and translate complex data concepts into practical solutions. - Communicate effectively with customers and stakeholders, including those without a technical background. - Work independently in a fast-paced environment while adapting to changing priorities and project requirements. - Apply strong critical thinking and analytical skills to solve complex data engineering challenges. - Support data governance and data management best practices across the organization. - Utilize DevOps practices and tools, including Azure DevOps, to support efficient development and deployment processes. - Contribute to the development of modern data and analytics capabilities that support business intelligence, forecasting, and AI initiatives. Requirements - At least 4 years of detail oriented experience in Data Engineering or a related field. - Strong hands-on experience with Microsoft Azure cloud environments and data engineering solutions. - Strong
experience with Databricks, including Databricks Pipelines/Workflows and production data environments. - Proficiency in Python, SQL, and PySpark. - Experience designing, developing, and optimizing ETL/ELT data pipelines. - Strong understanding of data management, data quality, and data modeling principles. - Experience working with both structured and unstructured data. - Knowledge of the Apache Iceberg file format and modern data architecture concepts. - Demonstrated ability to troubleshoot data pipelines, identify performance bottlenecks, and optimize processes to meet SLAs. - Experience working with data pipelines supporting machine learning, predictive analytics, or data science initiatives is highly preferred. - Experience with Azure DevOps and Microsoft Fabric is a plus. - Strong analytical and critical-thinking skills with the ability to approach complex technical problems independently. - Strong communication and interpersonal skills, with the ability to translate
technical data concepts for business and customer stakeholders. - Ability to work effectively in a fast-paced environment, adapt to changing priorities, and take initiative with minimal direction. - CPG, retail, or other high-volume data environment experience is a plus. Technology Doesn't Change the World, People Do.

## Description

Responsibilities: - Design, develop, maintain, and optimize scalable data pipelines and ETL/ELT processes. - Troubleshoot data pipelines that are missing SLAs by identifying bottlenecks and implementing solutions to improve performance and processing efficiency. - Work with large volumes of structured and unstructured data while maintaining strong data quality and reliability. - Develop and optimize data solutions using Python, SQL, PySpark, Azure, and Databricks. - Build and manage Databricks pipelines and workflows within a production environment. - Rework and improve existing data structures to support analytics, forecasting, and machine learning initiatives. - Partner closely with Data Scientists to develop and maintain data pipelines supporting predictive modeling and machine learning projects. - Analyze existing data environments and proactively identify opportunities to improve performance, reliability, and data quality. - Collaborate with technical teams and business


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