Analytics Engineers sit at the intersection of data engineering and data analysis, transforming raw data into clean, tested, and documented datasets that analysts and business users can rely on. They design data models, build and maintain transformation pipelines (often using tools like dbt), enforce data quality standards, and ensure metrics are defined consistently across an organization. This role emerged as companies adopted modern cloud data warehouses and needed someone to bridge the gap between engineers who move data and analysts who interpret it.
| Entry level | $85,000 |
| Median | $125,000 |
| Senior | $160,000 |
| Top 10% | $195,000 |
| Job growth | +23% |
| Professionals in the USA | 0.15 million |
| Typical hours/week | 42 hrs |
| Remote work share | 60% |
| Annual job openings | 18,000/yr |
| Demand | Very High |
AI-powered tools are increasingly automating data transformation, testing, and documentation tasks that once consumed much of an analytics engineer's time. However, the role's emphasis on designing data models, ensuring business logic accuracy, and bridging data teams with stakeholders keeps it resilient. The job is shifting toward higher-level architecture and governance rather than disappearing.
Automation exposure: SQL code generation, boilerplate dbt model creation, documentation writing, basic data quality checks, and repetitive ETL/ELT scripting are increasingly automated by AI copilots and tools.
The human edge: Understanding nuanced business context, designing scalable data architectures, making judgment calls on data modeling tradeoffs, and communicating technical concepts to non-technical stakeholders remain distinctly human strengths.
Figures are estimates for exploration — verify current data with BLS.gov.