Can you describe the architecture of your data pipelines? How do you develop, deploy, and productionize them, and what infrastructure do you use?

A robust data pipeline architecture typically follows a modular, stage‑based design. First, data ingestion pulls raw data from sources (APIs, logs, databases) into a landing zone, often using tools like Kafka, Flume, or S3. Next, a transformation layer cleans, enriches, and aggregates data; this can be batch (Spark, Flink) or streaming (Kafka Streams, Structured Streaming). The processed data is then stored in a data lake or warehouse (S3, Delta Lake, Snowflake). Finally, a serving layer exposes the data to downstream consumers via APIs, BI dashboards, or downstream pipelines. Deployment is managed through CI/CD pipelines (Jenkins, GitLab CI, ArgoCD) that build Docker images, run unit and integration tests, and promote artifacts to staging and production. Productionization involves monitoring (Prometheus, Grafana), alerting, automated rollback, and schema evolution handling. Infrastructure is usually cloud‑native: Kubernetes for orchestration, managed services for storage, and IaC (Terraform, CloudFormation) for reproducibility.