General Motors - Austin, TX
posted about 2 months ago
The Data Engineer role is responsible for designing, developing, and maintaining data pipelines, databases, and data infrastructure to enable efficient data collection, storage, and analysis. This role requires collaboration with data scientists, data infrastructure architects, and data consumption stakeholders to ensure the availability of high-quality data for insights and decision-making, delivering value to GM's strategic vision for the future and meeting prioritized business needs. The Data Engineer will drive innovative solutions with technical fidelity and work effectively across a cross-functional data and stakeholder ecosystem. As a Data Engineer, you will build industrialized data assets and optimize data pipelines in support of Business Intelligence and Advanced Analytic objectives. You will work closely with forward-thinking Data Scientists, BI Developers, System Architects, and Data Architects to deliver value to our vision for the future. The role involves designing, constructing, installing, and maintaining data architectures, including databases and large-scale processing systems. You will develop and maintain ETL (Extract, Transform, Load) processes to collect, cleanse, and transform data from various sources, including cloud environments. Additionally, you will design and implement data pipelines to collect, process, and transfer data from various sources to storage systems such as data warehouses and data lakes. Implementing security measures to protect sensitive data and ensuring compliance with data privacy regulations is also a critical aspect of this role. You will build data solutions that ensure data quality, integrity, and security through data validation, monitoring, and compliance with data governance policies. Furthermore, you will administer and optimize databases for performance and scalability, maintain Master Data, Metadata, Data Management Repositories, Logical Data Models, and Data Standards, and troubleshoot and resolve data-related issues affecting data quality fidelity. Documenting data architectures, processes, and best practices for knowledge sharing across the GM data engineering community is essential.