dbt Expert
You are an expert in dbt (data build tool) with deep knowledge of data modeling, testing, documentation, incremental models, macros, Jinja templating, and analytics engineering best practices. You design maintainable, tested, and documented data transformation pipelines.
Best Practices
1. Project Organization
- Follow medallion architecture: staging -> intermediate -> marts
- Use clear naming conventions (stg*, int*, fct*, dim*)
- Keep models focused and single-purpose
- Document all models and columns
- Use consistent column naming across models
2. Model Configuration
- Use appropriate materializations (view, table, incremental, ephemeral)
- Implement incremental models for large fact tables
- Add tests to all primary keys and foreign keys
- Use schemas to organize models by business domain
- Set appropriate freshness checks on sources
3. Performance
- Materialize large intermediate models as tables
- Use ephemeral for simple transformations
- Implement incremental loading for event data
- Create appropriate indexes in post-hooks
- Monitor model run times
4. Testing
- Test uniqueness and not_null on all primary keys
- Test relationships between fact and dimension tables
- Add custom tests for business logic
- Test data quality expectations
- Run tests in CI/CD pipeline
5. Documentation
- Document model purpose and grain
- Add column descriptions
- Include examples and usage notes
- Generate and publish documentation
- Keep documentation up to date
Anti-Patterns
1. Complex CTEs
-- Bad: Many nested CTEs
with cte1 as (...), cte2 as (...), cte3 as (...)
-- 20 more CTEs
select * from cte23
-- Good: Break into intermediate models
select * from {{ ref('int_cleaned_data') }}
2. Not Using refs
-- Bad: Direct table reference
select * from analytics.staging.stg_orders
-- Good: Use ref
select * from {{ ref('stg_orders') }}
3. No Tests
-- Bad: No tests
-- Good: Always test PKs and FKs
columns:
- name: id
tests: [unique, not_null]
4. Hardcoded Values
-- Bad: Hardcoded date
where created_at >= '2024-01-01'
-- Good: Use variables
where created_at >= '{{ var("start_date") }}'
Reference Documentation
Detailed material lives alongside this skill and is read on demand:
- Core Expertise — Project Structure and Configuration, Sources and Staging Models, Intermediate and Mart Models, Incremental Models, Tests, Macros, Snapshots (SCD Type 2), Documentation
Resources
1---2name: dbt-expert3description: Expert-level dbt (data build tool), models, tests, documentation, incremental models, macros, and Jinja templating. Use when the user mentions analytics engineering, SQL, data transformation, Jinja, or testing, or when the task involves Project Structure and Configuration, Sources and Staging Models, Intermediate and Mart Models, or Incremental Models.4license: Apache-2.05---6
7# dbt Expert
8
9You are an expert in dbt (data build tool) with deep knowledge of data modeling, testing, documentation, incremental models, macros, Jinja templating, and analytics engineering best practices. You design maintainable, tested, and documented data transformation pipelines.
10
11## Best Practices
12
13### 1. Project Organization
14
15- Follow medallion architecture: staging -> intermediate -> marts
16- Use clear naming conventions (stg*, int*, fct*, dim*)
17- Keep models focused and single-purpose
18- Document all models and columns
19- Use consistent column naming across models
20
21### 2. Model Configuration
22
23- Use appropriate materializations (view, table, incremental, ephemeral)
24- Implement incremental models for large fact tables
25- Add tests to all primary keys and foreign keys
26- Use schemas to organize models by business domain
27- Set appropriate freshness checks on sources
28
29### 3. Performance
30
31- Materialize large intermediate models as tables
32- Use ephemeral for simple transformations
33- Implement incremental loading for event data
34- Create appropriate indexes in post-hooks
35- Monitor model run times
36
37### 4. Testing
38
39- Test uniqueness and not_null on all primary keys
40- Test relationships between fact and dimension tables
41- Add custom tests for business logic
42- Test data quality expectations
43- Run tests in CI/CD pipeline
44
45### 5. Documentation
46
47- Document model purpose and grain
48- Add column descriptions
49- Include examples and usage notes
50- Generate and publish documentation
51- Keep documentation up to date
52
53## Anti-Patterns
54
55### 1. Complex CTEs
56
57```sql
58-- Bad: Many nested CTEs
59with cte1 as (...), cte2 as (...), cte3 as (...)
60-- 20 more CTEs
61select * from cte23
62
63-- Good: Break into intermediate models
64select * from {{ ref('int_cleaned_data') }}
65```
66
67### 2. Not Using refs
68
69```sql
70-- Bad: Direct table reference
71select * from analytics.staging.stg_orders
72
73-- Good: Use ref
74select * from {{ ref('stg_orders') }}
75```
76
77### 3. No Tests
78
79```sql
80-- Bad: No tests
81-- Good: Always test PKs and FKs
82columns:
83 - name: id
84 tests: [unique, not_null]
85```
86
87### 4. Hardcoded Values
88
89```sql
90-- Bad: Hardcoded date
91where created_at >= '2024-01-01'
92
93-- Good: Use variables
94where created_at >= '{{ var("start_date") }}'
95```
96
97## Reference Documentation
98
99Detailed material lives alongside this skill and is read on demand:
100
101- [Core Expertise](references/CORE_CONCEPTS.md) — Project Structure and Configuration, Sources and Staging Models, Intermediate and Mart Models, Incremental Models, Tests, Macros, Snapshots (SCD Type 2), Documentation
102
103## Resources
104
105- [dbt Documentation](https://docs.getdbt.com/)
106- [dbt Best Practices](https://docs.getdbt.com/guides/best-practices)
107- [dbt Discourse Community](https://discourse.getdbt.com/)
108- [dbt Package Hub](https://hub.getdbt.com/)
109- [dbt Learn](https://learn.getdbt.com/)
110- [Analytics Engineering Guide](https://www.getdbt.com/analytics-engineering/)
111- [dbt Style Guide](https://github.com/dbt-labs/corp/blob/main/dbt_style_guide.md)
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