The hands-on Principal Data Architect is responsible for designing and architecting highly scalable data storage solutions and pipelines for vehicle data, manufacturing, sales, finance, and other data systems. If you enjoy architecting large-scale systems, working with a talented team of engineers, and collaborating with some of the brightest minds in the Automotive industry.
The Role:
Architectural Leadership: Lead the architecture, design, and development of big data streaming systems and business data consolidation based on business priorities and cost optimization.
IoT System Design: Design IoT systems to facilitate communication and data movement between vehicles and the cloud.
Technology Utilization: Leverage cutting-edge distributed big data technologies such as Docker, Kubernetes, Spark, MQTT, gRPC, rosbag, Kafka, S3 Data Lake, and video/audio processing.
Team Leadership: Act as a hands-on technical leader, guiding the team in making key architectural decisions, unblocking challenges, and ensuring successful execution.
Technical Direction and Roadmaps: Set technical direction and roadmaps, guiding the evolution of our data systems to meet future business needs.
Application Architecture: Collaborate on application architecture and deep understanding of cloud data applications.
Vision and Roadmap Development: Develop, communicate, and advocate for the long-term technical vision and roadmap for data storage, ingestion, and retrieval in collaboration with technical leads.
Strategic Vision for Data Integration: Drive the technical and strategic vision for our data platform and work closely with the infrastructure team to meet current and future scalability and interoperability needs.
Qualifications:
B.S. or M.S. in Computer Science or a related field.
10+ years of hands-on industry experience in data architecture or software engineering.
3+ years of experience architecting solutions for big data environments.
Deep knowledge of modern data/compute infrastructure and frameworks, such as Docker, Kubernetes, Spark, Kafka, S3 Data Lake, Parquet, rosbag, MQTT, and gRPC.
Proven experience in architecting streaming, real-time, and near real-time data pipelines using tools like Spark or Kafka.
Working knowledge of data lake architectures with Parquet and HCatalog.
Ability to cultivate a strong sense of ownership and build a culture of accountability within the team.
Openness to new ideas and the ability to evaluate multiple approaches, selecting the best based on fundamental qualities and supporting data.
Exceptional communication skills to articulate highly technical problems and solutions to diverse audiences, from engineers to executive leadership.
Proven track record of setting technical vision and delivering results in a collaborative, cross-functional environment.
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