Analytics Engineer
Analytics Engineers at AI companies sit between data engineering and analytics, building and maintaining the data models, metrics layers, and self-serve analytics that the rest of the company relies on to make decisions. The day-to-day is SQL- and dbt-heavy: designing dimensional schemas and warehouse models, defining metric logic that holds across teams, building documentation and tests, and partnering with finance, product, and GTM stakeholders on what the numbers should mean. Where the role differs from data engineering is in proximity to business questions—Analytics Engineers spend more time defining metrics and enabling self-service than building ingestion pipelines, even when the technical surface looks similar. Specific data domains range from product usage and revenue (most companies) to compute and infrastructure economics (at AI infrastructure companies), but the underlying methodology is the same.
Measured across 24 of 24 open postings.
This role is advertised at 2 levels, so a single figure for the role would describe none of them. Experience and pay are the midpoints for each level on its own.
| Level | Share | Median years | Median pay |
|---|---|---|---|
| Mid | 50%(12) | 4 | — |
| Senior | 42%(10) | 5 | — |
A dash means too few postings stated it to report a midpoint. Most companies do not publish a salary band, so pay is indicative rather than a market rate. 2 levels with fewer than 10 open postings are not shown.
“use Claude and AI tools as force multipliers in analysis”
“support AI-driven self-serve analysis, key metrics tracking, and external customer reporting”
“building their own content and analyses using Claude, connected to the same modeled data”
“using AI coding agents is a default part of how you build and maintain pipelines and dashboards”
Skills
What companies are looking for in this role.
Analytics data modeling
Data pipeline engineering
Data quality and governance
Dashboarding and self-service analytics
SQL query development
Business insight analysis
Metrics and KPI definition
Marketing and GTM measurement and attribution
Experimentation and causal inference
Monitoring and observability
Predictive modeling and forecasting
Product analytics & experimentation
AI-assisted development workflow
Financial and usage data modeling
ML platform engineering
Consumption-based pricing and revenue models
Cross-functional collaboration
Executive data storytelling
Technology
The tools and technologies that define this role.
Open Jobs
24 open Analytics Engineer jobs across 18 companies.
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