General Motors - Atlanta, GA
posted 2 months ago
The Marketing Applied Sciences team at General Motors is dedicated to developing analytics-driven solutions that empower GM organizations to achieve their business objectives. As a member of this multi-disciplinary team, you will engage in the design, development, and deployment of analytic models that support various business-facing analytic groups. This role emphasizes both advanced analytics strategy and applied, project-based solutions, focusing on the company's most critical business areas. In your capacity as a Data Engineer, you will be responsible for constructing analytical data sets that support Advanced Analytics projects. You will collaborate closely with innovative Researchers and Data Scientists to deliver value aligned with GM's vision for the future. This position reports to the Product Owner of Marketing Activation Science, with visibility to the broader Enterprise Data, Analytics, and Insights organization, which is committed to democratizing data and decision-making across the company. Your role will involve collaborating with internal and external stakeholders, including Activation Data Science, to fulfill the project portfolio. You will drive the adoption of cloud-first technologies and industry-standard Data Engineering practices to enhance and scale engineering capabilities. Adhering to established standards and processes, you will validate, monitor, and support data products that enable the decision science portfolio and data science capabilities. Additionally, you will work with cross-functional teams, including data governance, security, data architecture, release management, Dev & ML Ops, and infrastructure, to ensure seamless integration and alignment of data engineering initiatives. Staying abreast of emerging trends and technologies in Data Engineering will be crucial, as will proactively identifying opportunities for improvement and innovation within the organization. You will also participate in a culture of continuous learning, knowledge sharing, and development within the team and the broader data engineering community.