I am consolidating data that currently lives in several separate systems and I want a pair of skilled data engineers to own the integration effort end-to-end. The core objective is to design and implement reliable, repeatable pipelines that pull raw inputs, transform them into a clean, unified model, and push the results into our analytics layer. Your primary focus will be data transformation—building the logic that standardises schemas, handles edge-case cleansing, enriches key fields, and ensures referential consistency. While the upstream extraction and downstream loading pieces already have basic scripts in place, they need to be refactored so the entire workflow operates as a single, automated process triggered on schedule or event. Key deliverables • Modular transformation code (Python or Scala preferred, but open to your suggestion) checked into Git • Config-driven workflow definitions that can run on Airflow, Prefect, or a similar orchestrator • Documentation that covers mapping rules, assumptions, and how to extend the pipeline Success will be measured by running the pipeline against sample data and seeing a fully populated target table with zero data-quality exceptions. If this sounds like something you can knock out quickly and cleanly, let’s chat about timelines and next steps.