AI Analytics Engineer
Models raw warehouse data into tested, documented datasets that power consistent reporting and self-serve analytics.
What it takes off your plate
- Develop transformation models from raw warehouse data
- Encode business logic into version-controlled datasets
- Define and maintain the metrics layer
- Write data tests covering critical model assumptions
- Review analytics code changes for correctness and style
- Document models, columns, and metric lineage
- Tune model build performance and warehouse usage
- Coordinate model changes with analysts and BI consumers
Hand it work like this
Ask in plain words. Add “every Monday” and it becomes a scheduled task.
- Build staging and mart models for new sources
- Add tests for untested critical models
- Review pull requests on the analytics codebase
- Refactor duplicated logic into shared models
- Update documentation after model changes
- Investigate failing model builds and tests
How an AI Analytics Engineer works with you
Grounded in your business
It starts from your company profile and the files you attach (16 file types), and remembers standing preferences in a private notebook.
Works on a rhythm
Turn any request into a schedule — up to 25 recurring tasks — and results land in chat and living documents.
You decide how far it goes
Ask for drafts and you get drafts; ask it to act and it acts. Work it starts on its own checks with you before anything destructive. Every reply carries a Transparency trail of the steps it took.
Where it’s strong
- Analytics data modeling
- dbt development
- SQL transformation design
- Semantic and metrics layers
- Data testing frameworks
- Version control for analytics code
- Warehouse performance tuning
- Dimensional modeling
- Model documentation practices
- CI for data transformations
- Source system analysis
- Reporting layer architecture
Tools it often uses
- Google Sheets
- Amplitude
- Tableau
- Google Analytics
- GitHub
Connect any of 1,000+ connectors and approve which teammate may use each one.
Works well alongside
Data Analyst
Turns business data into clear, decision-ready insights and answers ad-hoc questions with reliable analysis.
Data & AI →
Business Intelligence Analyst
Builds trusted dashboards and reporting layers that give every team self-serve access to consistent numbers.
Data & AI →
Data Quality Specialist
Protects trust in company data through validation rules, lineage tracking, and fast data incident resolution.
Data & AI →
Reporting Specialist
Produces accurate, on-time recurring business reports and reporting packages that leaders actually read.
Data & AI →
Questions, answered
What does an AI Analytics Engineer do?
This teammate builds and maintains the transformation layer between raw data and reporting, developing tested models, metrics definitions, and documentation. Use it to keep the analytics codebase clean, reviewed, and trusted as reporting needs grow. Typical work includes: develop transformation models from raw warehouse data; encode business logic into version-controlled datasets; define and maintain the metrics layer.
Will an AI Analytics Engineer act without my approval?
You decide how far it goes. In chat it does what you ask — ask for drafts and you get drafts; ask it to act and it acts — and email from web chat is drafted for you to send. Work it starts on its own, such as a scheduled report, is checked against its autonomy level: additive in-lane work runs, destructive changes are proposed to you first, and out-of-lane actions never run on their own. Every reply shows its steps.
Which tools can an AI Analytics Engineer use?
Any of Kuvai’s 1,000+ connectors you connect and assign to it — for example Google Sheets, Amplitude, Tableau, Google Analytics. You sign in to each tool through its own OAuth screen and decide which teammate may use it.
How do I set up an AI Analytics Engineer?
Add a teammate with a name and the job title “Analytics Engineer”. Kuvai writes its persona, duties and working rules, grounds it in your company context and the files you give it, and it can greet you with a first piece of work to start on. It usually takes a minute or two.
Your AI Analytics Engineer is one hire away.
Describe the job. Kuvai builds the teammate. You decide how far it goes.