Software Engineer II - Streaming

Rivian and Volkswagen Group Technologies•Palo Alto, CA

3d•$116,300 - $154,130•Onsite

About The Position

Rivian and Volkswagen Group Technologies is a joint venture between two industry leaders with a clear vision for automotive’s next chapter. From operating systems to zonal controllers to cloud and connectivity solutions, we’re addressing the challenges of electric vehicles through technology that will set the standards for software-defined vehicles around the world. The road to the future is uncharted. By combining our expertise across connectivity, AI, security and more, we’ll map a new way forward. Working together, we’ll create a future that’s more connected, more intelligent, more sustainable for everyone. Rivian and Volkswagen Group Technologies are seeking a Software Engineer II to join the Data Platform Streaming team in Palo Alto, California. We are looking for strong software engineers with proven experience building and operating modern real-time data platforms. This team develops the streaming infrastructure that powers vehicle telemetry, connected services, analytics, AI/ML platforms, and commercial fleet integrations across Rivian and Volkswagen Group Technologies. This is a hands on software engineering role focused on designing and building scalable, low-latency, fault tolerant streaming systems. The ideal candidate has strong experience with Apache Kafka or Redpanda, Apache Flink, distributed systems, and cloud native technologies. They are passionate about building reliable real-time data infrastructure that enables event-driven applications, machine learning, AI-powered experiences, and intelligent vehicle platforms. At the RIV-4 level, engineers are expected to demonstrate strong technical fundamentals, contribute meaningfully to team objectives, work with increasing autonomy, and collaborate effectively across engineering teams while continuously growing their technical expertise.

Requirements

Bachelor's or Master's degree in Computer Science, Software Engineering, or a related technical field, or equivalent practical experience.
3+ years of professional software engineering experience building distributed systems and real-time streaming platforms.
Strong hands-on experience with Apache Kafka or Redpanda, including: Topics and partitions, Consumer groups, Replication, Delivery semantics, Producer and consumer tuning, Performance optimization, Production operations
Strong experience building production applications using Apache Flink, including: Stateful stream processing, Event time processing, Watermarks, Windowing, Checkpointing, Savepoints, Parallelism, Backpressure, Fault recovery
Strong understanding of event-driven architectures and real-time streaming systems.
Solid understanding of distributed systems including scalability, fault tolerance, consistency models, replication, and performance optimization.
Proficiency in one or more programming languages such as Java, Scala, Python, or Go.
Strong software engineering fundamentals including data structures, algorithms, object-oriented design, and system design.
Experience building cloud-native applications using Docker and Kubernetes.
Experience working with public cloud platforms such as AWS, Azure, or GCP.
Experience debugging production issues across distributed applications and streaming infrastructure.
Familiarity with REST APIs, gRPC, or event-driven service integration.
Strong communication and collaboration skills with the ability to work effectively across cross-

Nice To Haves

Experience operating large-scale Apache Kafka and Apache Flink deployments in production.
Experience with Kafka Connect, Debezium, or Change Data Capture (CDC) architectures.
Experience designing schema-based event contracts using Avro, Protobuf, JSON Schema, and Schema Registry.
Experience integrating streaming platforms with Apache Iceberg, Delta Lake, ClickHouse, Apache Pinot, or similar analytical storage systems.
Familiarity with Spark Structured Streaming or complementary stream processing technologies.
Experience building real-time feature engineering pipelines supporting machine learning and AI applications.
Experience with OpenTelemetry, Prometheus, Grafana, distributed tracing, and modern observability platforms.
Experience using Terraform, Helm, or Infrastructure as Code practices.
Experience building edge-to-cloud or vehicle-to-cloud streaming architectures.
Experience participating in production incident response, postmortems, and operational excellence initiatives.

Responsibilities

Build and maintain scalable real-time streaming services that ingest, process, transform, and deliver vehicle, cloud, and operational data at scale.
Design, develop, and operate Apache Flink applications and event-driven services using Apache Kafka or Redpanda.
Build streaming infrastructure that powers analytics, machine learning, AI applications, and real-time operational decision making.
Design scalable event-driven architectures with emphasis on throughput, low latency, resiliency, and operational simplicity.
Develop production-grade streaming applications using Java, Scala, Python, or Go following software engineering best practices.
Contribute to scalable streaming architectures including topic design, partitioning strategy, checkpointing, savepoints, dead letter queues (DLQs), replay strategies, and failure recovery.
Optimize streaming applications for latency, throughput, resource utilization, and operational cost through performance tuning and continuous improvement.
Develop reusable platform components, SDKs, libraries, and self-service capabilities that improve developer productivity across engineering teams.
Support deployment and operations using K8s, Docker, CI/CD pipelines, and Infrastructure as Code practices.
Build observability into streaming applications using metrics, logging, distributed tracing, dashboards, and proactive alerting.
Monitor and troubleshoot production streaming systems using consumer lag, checkpoint health, logs, metrics, and operational dashboards.
Partner closely with platform engineers, data engineers, ML engineers, analytics teams, and product teams to deliver end-to-end real-time data solutions.
Build streaming pipelines that enable AI/ML platforms, online feature engineering, retrieval-augmented generation (RAG), and intelligent applications.
Participate in production support, incident response, root cause analysis, and continuous platform reliability improvements.
Leverage AI-assisted software development tools to improve code quality, testing, debugging, documentation, and engineering productivity while maintaining strong software engineering practices.