ETL Developers design, build, and maintain the systems that move data from source systems—like transactional databases, APIs, and third-party platforms—into data warehouses or lakes where it can be analyzed. They write scripts and configure tools to extract raw data, transform it into clean, structured formats, and load it into destinations used by analysts, data scientists, and business intelligence teams. Their work ensures that reports, dashboards, and machine learning models are built on accurate, timely, and consistent data.
| Entry level | $68,000 |
| Median | $98,000 |
| Senior | $128,000 |
| Top 10% | $155,000 |
| Job growth | +23% |
| Professionals in the USA | 0.5 million |
| Typical hours/week | 42 hrs |
| Remote work share | 60% |
| Annual job openings | 45,000/yr |
| Demand | Very High |
AI and low-code tools are automating many repetitive ETL tasks like schema mapping, data cleansing, and pipeline generation. However, complex data architecture design, business logic implementation, and troubleshooting still require human expertise. The role is shifting toward higher-level data engineering and orchestration rather than being eliminated.
Automation exposure: AI can automate boilerplate code generation, basic data transformation scripts, schema mapping suggestions, anomaly detection in data quality, and routine pipeline monitoring alerts.
The human edge: Humans excel at understanding nuanced business requirements, designing scalable data architectures, making judgment calls on data governance and compliance, and debugging complex, novel pipeline failures that AI tools haven't encountered before.
Figures are estimates for exploration — verify current data with BLS.gov.