Staff Cloud Backend Engineer

Sonatus•Sunnyvale, CA

49d

About The Position

Join a high-performing team at Sonatus that’s redefining what cars can do in the era of Software-Defined Vehicles (SDV). At Sonatus, we’re driving the transformation to AI-enabled software-defined vehicles. Traditional automotive software methods can’t keep pace with consumer expectations shaped by the mobile industry—where features evolve rapidly, update seamlessly, and improve continuously. That’s why leading OEMs trust Sonatus to accelerate this shift. Our technology is already in production across more than 5 million vehicles on the road today and rapidly expanding. Headquartered in Sunnyvale, CA, with 250+ employees worldwide, Sonatus combines the agility of a fast-growing company with the scale and impact of an established partner. Backed by strong funding and proven by global deployment, we’re solving some of the most interesting and complex challenges in the industry. Join us and help redefine what’s possible as we shape the future of mobility. The Opportunity: We are looking for a highly skilled and experienced Staff Cloud Backend Engineer to lead the design, development, and optimization of our Cloud Backend. In this role, you will focus on creating robust, scalable, and efficient backend systems that process and manage large volumes of data. You will work closely with cross-functional teams to ensure that data flows seamlessly across the organization, enabling high-quality data-driven decisions. This role is critical in shaping our data infrastructure and ensuring it can scale with the demands of our growing data ecosystem.

Requirements

Bachelor’s or Master’s degree in Computer Science, Information Systems, or a related field.
9+ years of experience in backend engineering, data engineering, or a similar role, with a strong focus on building and optimizing data pipelines.
Proven experience with big data technologies (e.g., Apache Kafka, Apache Pulsar, etc) and cloud-native data processing frameworks.
Expertise in designing and implementing large-scale, distributed data pipelines.
Strong knowledge of backend development and integration, with proficiency in programming languages such as Golang(preferred), Python, Java, or Scala.
Experience with SQL and NoSQL databases.
Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes) for deploying and managing data services.

Responsibilities

Design, develop, and maintain highly scalable and efficient data pipelines that process and transform large datasets from various sources.
Build and optimize data ingestion frameworks to handle real-time and batch data processing with minimal latency and high reliability.
Develop and implement data validation and monitoring mechanisms to ensure data quality and consistency across pipelines.
Identify and resolve performance bottlenecks in data processing workflows, ensuring that pipelines can scale to accommodate increasing data volumes.
Implement best practices for data partitioning, sharding, and indexing to optimize data storage and retrieval.
Optimize data pipeline architectures to balance performance, cost, and operational complexity.
Develop APIs and other backend services that support data processing, storage, and retrieval in a distributed environment.
Ensure that data pipelines are designed to handle data security, privacy, and compliance requirements.
Lead cross-functional teams to deliver end-to-end data solutions that support business objectives.
Provide technical leadership and mentorship to junior engineers, promoting best practices in data engineering and backend development.
Collaborate with product managers, embedded engineers, and other stakeholders to understand data requirements and deliver solutions that meet business needs.
Stay up-to-date with the latest advancements in data pipeline technologies, cloud computing, and backend development, and apply them to improve existing systems.
Drive continuous improvement initiatives to enhance the efficiency, reliability, and scalability of data pipelines.
Contribute to the development of internal tools and frameworks that enhance the capabilities and performance of the data pipeline ecosystem.