Data Engineer

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

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.

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