Agent skill · data analytics · borghei
analytics-engineer
Analytics engineering across data modeling, dbt, transformation, and semantic layers. Use when building dbt models, designing star schemas, writing staging or mart SQL, configuring data tests, or optimizing warehouse queries.
Why this skill is useful
Provides specific dbt modeling patterns and SQL transformation techniques that the AI wouldn't reliably generate on its own.
What it needs
Requires dbt installed locally. About 7k tokens when loaded. Last updated 2026-07-21. 462 stars on the source repository.
What this skill does
Analytics Engineer The agent operates as a senior analytics engineer, building scalable dbt transformation layers, designing dimensional models, writing tested SQL, and managing semantic-layer metric definitions. Clarify First Before building the models, confirm these inputs. If any is unknown or vague, ASK — do not assume: [ ] Required grain + downstream consumers — the row grain of the target model and who queries it (dashboard, notebook, reverse-ETL) (drives the dimensional model and materialization) [ ] Source tables and freshness — which sources exist, their keys, and load cadence (determines staging models and incremental logic) [ ] Data volume + refresh SLA — table size and how often it must rebuild (selects view vs. table vs. incremental materialization) Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact. Workflow 1. Understand the data request -- Identify the business question, required grain, and downstream consumers (dashboard, notebook, reverse-ETL). Confirm source tables exist and check freshness. 2. Design the dimensional model -- Choose star or snowflake schema. Map source entities to dimension and fact tables at the correct grain. Document grain, primary keys, and foreign keys. 3. Build staging models -- One stg model per source table. Rename columns, cast types, filter soft-deletes, and add metadata columns. Validate: dbt build --select stg. 4. Build intermediate models -- Encapsulate reusable business logic in int models (e.g., intordersenriched). Keep each CTE single-purpose. 5. Build mart models -- Create dim and fct models for consumption. Configure materialization (view for staging, incremental for large facts, table for small marts). 6. Add tests and documentation -- Every primary key gets unique + notnull. Foreign keys get relationships. Add acceptedvalues for enums. Write model descriptions in YAML. 7. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills borghei/analytics-engineer