How would you explain a modern data pipeline for this Peabody apprenticeship role?

08 July 2026

Question

How would you explain how data is collected, transformed and moved through a modern data platform to support business outcomes?

I would explain it as a staged flow from source systems to trusted business-ready data products.

Ingestion: Data starts in operational systems such as housing, repairs, finance or customer service platforms. I would use a tool such as Azure Data Factory to schedule and orchestrate ingestion into Azure Data Lake Gen2. - Raw Landing Layer: The first step is to keep the data in a raw or bronze layer so we preserve the original records, support traceability and make reprocessing possible. - Transformation: I would use Databricks with Python and SQL to clean, standardise and join the data into a silver layer. This is where I would handle nulls, duplicates, schema issues and business rules. - Business Curation: The gold layer would contain curated datasets designed for reporting, analytics or downstream applications. For Peabody, that might mean trusted views for tenancy, repairs performance or resident outcomes. - Quality and Monitoring: At each stage I would add validation checks, logging and alerts so failures are visible early and poor-quality data does not move downstream unchecked. - Documentation and Governance: I would document dataset purpose, lineage, ownership and definitions so analysts and stakeholders understand what they are using. The main point is that a pipeline is not just moving data. It is turning raw source data into reliable, reusable data products that help the organisation make better decisions.