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, with flexible schedule for periodic race weekend remote support.
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:
As a Senior Data Engineer, you will build industrialized data assets and optimize data pipelines in support of Business Intelligence and Advance Analytic objectives. You will work closely with our forward-thinking Data Scientists, BI Developers, System Architects and Data Architects to deliver value to our vision for the future. Are you ready to join a future facing team?
What You'll Do (Responsibilities):
- Communicates and maintains Master Data, Metadata, Data Management Repositories, Logical Data Models, Data Standards.
- Create and maintain optimal data pipeline architecture.
- Assemble large, complex data sets that meet functional / non-functional business requirements.
- Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
- Build industrialized analytic datasets and delivery mechanisms that utilize the data pipeline to deliver actionable insights into customer acquisition, operational efficiency and other key business performance metrics.
- Work with business partners on data-related technical issues and develop requirements to support their data infrastructure needs.
- Create highly consistent and accurate analytic datasets suitable for business intelligence and data scientist team members.
