I’m looking for a hands-on data engineer who can give me an initial, working pipeline on Azure. The core of the job is to set up a small but scalable pattern that I can extend later. Here’s what I need right now: • Design a basic end-to-end pipeline in Azure, from raw data ingestion to a modeled table in the warehouse. • Use dbt for the transformation layer and show me how to structure models, tests, and documentation. • Demonstrate streaming ingestion with Apache Kafka (I also have GCP Datastream and AWS Kinesis available, so the design should keep them in mind). • Write clean, reusable Python and SQL code that I can follow. • Wire the whole flow into a simple CI/CD routine—Terraform for infrastructure and a lightweight Git-based deployment step are fine. • Produce concise setup instructions and a quick walkthrough so I can run, modify, and redeploy the pipeline myself. I value clear communication, proactive problem solving, and an ownership mindset. If you’ve built cloud-native data systems before and enjoy delivering neat, well-explained solutions, let’s get started.