Most AI failures are data failures. The data engineer builds the pipelines and infrastructure that feed models clean, reliable, accessible data, the unglamorous work that makes everything else possible.
What they do
- Build and maintain data pipelines.
- Integrate sources into a usable warehouse or lake.
- Ensure data quality, freshness and access.
- Support ML and analytics with reliable data.
When to hire one
- Your data is scattered and inconsistent.
- ML or analytics work stalls on data access.
- Manual data wrangling eats your team's time.
