What Data Engineers Actually Do

AI doesn't replace data engineers. AI depends on them. Every model, dashboard, and automated decision runs on data that someone had to collect, clean, and organize. Data engineers are not competing with AI. They are building the infrastructure it needs to function.

This playbook covers how data pipelines actually work, the two main paradigms you'll encounter in the industry, and includes a production-ready template you can use to build your own like this one:

How to Build a Meta Ads Insight Pipeline

This playbook demonstrates how to build a modern Python data pipeline that extracts Meta Ads data to BigQuery, eliminating CSV exports and creating the warehouse foundation needed to reconcile platform-reported conversions with actual revenue across Meta, GA4, and sales data from a CRM.

RabbitmetricsBenjamin Falkeborg

What a pipeline does: A data pipeline moves information from where it's generated to where it's useful. That journey has three stages: ingestion (pulling data from APIs, databases, or files), processing (cleaning, filtering, aggregating, and enriching), and storage (loading it into a warehouse or lake where it can be queried). The goal is to make raw, messy data usable for analysis and decision-making.

How value is created: Raw data isn't worth much on its own. Value comes from refinement. The bronze, silver, and gold model gives you a framework for this: bronze holds raw ingested data, silver is cleaned and validated, and gold is aggregated and business-ready. Each layer builds on the last, transforming noise into insight.