Manager, Data & Platform Engineering - Motorsports

Workplace Classification: Hybrid: This position does not require an employee to be on-site full-time to perform most effectively. This position requires an employee to be onsite up to 3 times per week at their primary work location (Charlotte, Austin, Roswell, or Warren). The Team: GM’s Motorsports Software team analyzes, defines, and delivers next generation groundbreaking Motorsports IT software solutions. Using both innovative cloud-based infrastructure and software development standards, these solutions enable innovative interactions between GM Global Engineering, GM Motorsports, and our Race teams that accelerate our drivers to the finish line first! Our combined team of analysts, architects, developers, data engineers, testers, and product managers work with GM Motorsports Engineering and Racing teams to ensure podium wins for GM’s Formula 1, NASCAR, IndyCar, and IMSA&WEC sportscar teams! The Role: GM Motorsports is looking for a highly motivated and experienced Data Engineering Manager to join our dynamic team. In this role, you will oversee the design, implementation, and optimization of our data infrastructure, ensuring our data pipelines are efficient, scalable, and secure. You will lead a team of data engineers while collaborating closely with cross-functional teams to ensure the availability, quality, and accuracy of our data systems. Your expertise will help drive the data strategy and support data-driven decision-making across the organization. What You'll Do (Responsibilities): • Team Leadership: Lead, mentor, and manage a team of data engineers, fostering an environment of collaboration, continuous learning, and career development. • Data Architecture: Oversee the design and development of robust, scalable, and high-performance data pipelines and data architectures that meet business needs. • Project Management: Lead the planning, execution, and delivery of data engineering projects, ensuring alignment with business priorities and timelines. • Collaboration: Work closely with product, analytics, and data science teams to understand data requirements and deliver solutions that enable key business insights. • Optimization: Continuously monitor and optimize data systems and pipelines for performance, scalability, and cost-effectiveness. • Data Governance & Quality: Implement best practices for data governance, ensuring data accuracy, consistency, security, and privacy. • Tool Selection: Evaluate and recommend new tools and technologies for data processing, storage, and integration, ensuring the team uses the best tools for the job. • Stakeholder Communication: Provide regular updates to stakeholders on data engineering initiatives, challenges, and successes. • Innovation: Stay up-to-date with the latest trends and technologies in data engineering and big data platforms, and drive innovation within the team.

Responsibilities

• In this role, you will oversee the design, implementation, and optimization of our data infrastructure, ensuring our data pipelines are efficient, scalable, and secure

• You will lead a team of data engineers while collaborating closely with cross-functional teams to ensure the availability, quality, and accuracy of our data systems

• Your expertise will help drive the data strategy and support data-driven decision-making across the organization

• Team Leadership: Lead, mentor, and manage a team of data engineers, fostering an environment of collaboration, continuous learning, and career development

• Data Architecture: Oversee the design and development of robust, scalable, and high-performance data pipelines and data architectures that meet business needs

• Project Management: Lead the planning, execution, and delivery of data engineering projects, ensuring alignment with business priorities and timelines

• Collaboration: Work closely with product, analytics, and data science teams to understand data requirements and deliver solutions that enable key business insights

• Optimization: Continuously monitor and optimize data systems and pipelines for performance, scalability, and cost-effectiveness

• Data Governance & Quality: Implement best practices for data governance, ensuring data accuracy, consistency, security, and privacy

• Tool Selection: Evaluate and recommend new tools and technologies for data processing, storage, and integration, ensuring the team uses the best tools for the job

• Stakeholder Communication: Provide regular updates to stakeholders on data engineering initiatives, challenges, and successes

• Innovation: Stay up-to-date with the latest trends and technologies in data engineering and big data platforms, and drive innovation within the team