Yoda Response: Experience with developing data pipelines using Kafka, I have. In my current role, transitioning from AWS Kinesis to Kafka for better scalability and cost efficiency, we are. 

Using Kafka, our source systems push data directly to Kafka, handling billions of articles. This data, we process using Apache Beam. For scalability, deploying our services on Kubernetes, we are. Kubernetes allows us to scale based on load, using tools like KEDA and Carpenter for autoscaling. 

Ensuring the Kafka cluster is scalable, we can easily increase the number of machines based on data volume. For instance, from 10 to 20 machines, we can scale, each with significant storage capacity. This flexibility ensures our pipelines are both scalable and maintainable, handling large volumes of data efficiently. 

Maintainability, we achieve by using Prometheus for monitoring and alerting, ensuring our systems run smoothly. Thus, scalable and maintainable our data pipelines are, with Kafka and Kubernetes at the core.