A specialized skill for automating data transformation, dimensional modeling, and modern data stack orchestration.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install ah-analytics-engineer
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install ah-analytics-engineer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Analytics Engineer?
The Analytics Engineer skill transforms your AI agent into a professional data architect capable of bridging the gap between raw data engineering and business intelligence. By leveraging this addition to your library of Openclaw Skills, you can automate the creation of robust, tested, and documented data models using industry-standard tools like dbt and SQL. It focuses on modularity and scalability, ensuring your data warehouse remains performant and your analytics stay reliable.
This skill is designed for teams implementing a modern data stack, providing deep expertise in Snowflake, BigQuery, and Redshift environments. It streamlines the entire lifecycle of data modeling, from initial source profiling to the implementation of complex business logic and final BI tool integration, all while maintaining strict version control and governance standards.
Analytics Engineer Use Cases
- Designing and implementing modular dbt project structures for scalable data warehouses.
- Developing dimensional models, star schemas, and data marts based on business requirements.
- Automating data quality testing and validation frameworks to ensure reliable reporting.
- Setting up data pipeline orchestration and monitoring using Airflow, Prefect, or Dagster.
- Establishing comprehensive data governance, lineage tracking, and documentation for self-service analytics.
How Analytics Engineer Works
- Source Profiling: The skill analyzes source data to understand schema relationships, data types, and potential quality issues.
- Architecture Design: It designs dimensional models and defines the necessary materialization strategies for the target warehouse.
- Model Development: The agent generates modular SQL or Python models following dbt best practices and naming conventions.
- Quality Assurance: Automated tests and documentation are implemented to validate data integrity and logic.
- Deployment & Orchestration: The skill configures orchestration scripts and monitoring alerts to manage production data workflows.
Analytics Engineer Setup
To integrate this capability into your workflow, use the standard installation command for Openclaw Skills:
# Install the analytics engineer skill
openclaw install analytics-engineer
# Initialize your dbt project structure
openclaw run analytics-engineer --init-project
Analytics Engineer Data Schema & Taxonomy
The skill organizes data and metadata through a structured hierarchy optimized for dbt projects:
| Component | Format | Description |
|---|---|---|
| Transformation Models | .sql / .py | Modular code for staging, intermediate, and mart layers. |
| Configuration | .yml | Project settings, materialization rules, and variable definitions. |
| Documentation | .md / .yml | Column descriptions, model purposes, and business logic explanations. |
| Test Suites | .yml / .sql | Data quality assertions and custom validation macros. |
| Lineage | JSON | Metadata mapping dependencies between source tables and final models. |
Analytics Engineer Advanced Features
- Support for incremental materialization and clustering optimization to reduce warehouse costs.
- Automated data quality scorecards and SLA monitoring for production environments.
- Multi-warehouse compatibility across Snowflake, BigQuery, Databricks, and Redshift.
- Custom macro generation for reusable transformation logic and cross-database compatibility.
- Integration with BI tools like Tableau and Looker via automated exposure definitions.
SKILL.md
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