Automate data pipeline orchestration using Apache Airflow

## Problem
Each data source in the project currently follows a similar workflow that involves fetching, processing, and report generation. However, these steps are executed through individual scripts, which can make the process difficult to automate, monitor, and maintain — especially when scaling to multiple data sources.

## Description
I propose using Apache Airflow to orchestrate and automate the data workflows.
Each data source can be represented as an Airflow DAG, with tasks corresponding to each stage:

- Fetch: Collect data from APIs or external sources.

- Process: Clean, transform, and aggregate the data.

- Report: Generate summaries or metrics for analysis.

This approach would improve visibility, scheduling, error handling, and reusability across all data pipelines.

## Alternatives

- Continue using manual script execution

- Implement lightweight scheduling within Python scripts.

## Additional context
This integration aligns with the existing three-phase workflow structure and can easily wrap around current scripts without major codebase refactoring. Airflow also supports modular task development, which would benefit future contributors.

## Implementation

- [x] I would be interested in implementing this feature.

No specific files, tests, or entry points are named. Start by locating the existing fetch, process, and report scripts for each data source, then determine how they could map to Airflow DAG tasks; done would mean the workflows are scheduled, observable, and support error handling without disrupting the existing phases.

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