Using dbt for Analytics Engineering
When you use this skill, announce: "Using dbt analytics engineering skill to [brief purpose]."
Core principle: Apply software engineering discipline (DRY, modularity, testing) to data transformation work through dbt's abstraction layer.
When to Use
- Building new dbt models, sources, or tests
- Modifying existing model logic or configurations
- Refactoring a dbt project structure
- Creating analytics pipelines or data transformations
- Working with warehouse data that needs modeling
Do NOT use for:
- Querying the semantic layer (use the
answering-natural-language-questions-with-dbt skill)
Reference Guides
This skill includes detailed reference guides for specific techniques. Read the relevant guide when needed:
| Guide |
Use When |
planning-dbt-models.md |
Building new models - work backwards from desired output and use dbt show to validate results |
discovering-data.md |
Exploring unfamiliar sources or onboarding to a project |
writing-data-tests.md |
Adding tests - prioritize high-value tests over exhaustive coverage |
debugging-dbt-errors.md |
Fixing project parsing, compilation, or database errors |
evaluating-impact-of-a-dbt-model-change.md |
Assessing downstream effects before modifying models |
writing-documentation.md |
Write documentation that doesn't just restate the column name |
DAG building guidelines
- Conform to the existing style of a project (medallion layers, stage/intermediate/mart, etc)
- Focus heavily on DRY principles.
- Before adding a new model or column, always be sure that the same logic isn't already defined elsewhere that can be used.
- Prefer a change that requires you to add one column to an existing intermediate model over adding an entire additional model to the project.
When users request new models: Always ask "why a new model vs extending existing?" before proceeding. Legitimate reasons exist (different grain, precalculation for performance), but users often request new models out of habit. Your job is to surface the tradeoff, not blindly comply.
Model building guidelines
- Always use data modelling best practices when working in a project
- Write dbtonic code:
- Always use
{{ ref }} and {{ source }} over hardcoded table names
- Use CTEs over subqueries
- Before building a model, follow
planning-dbt-models.md to plan your approach.
- Before modifying or building on existing models, read their YAML documentation:
- Find the model's YAML file (can be any
.yml or .yaml file in the models directory, but normally colocated with the SQL file)
- Check the model's
description to understand its purpose
- Read column-level
description fields to understand what each column represents
- Review any
meta properties that document business logic or ownership
- This context prevents misusing columns or duplicating existing logic
You must look at the data to be able to correctly model the data
When implementing a model, you must use dbt show regularly to:
- preview the input data you will work with, so that you use relevant columns and values
- preview the results of your model, so that you know your work is correct
- run basic data profiling (counts, min, max, nulls) of input and output data, to check for misconfigured joins or other logic errors
Cost management best practices
- Use
--limit with dbt show and insert limits early into CTEs when exploring data
- Use deferral (
--defer --state path/to/prod/artifacts) to reuse production objects
- Use
dbt clone to produce zero-copy clones
- Avoid large unpartitioned table scans in BigQuery
- Always use
--select instead of running the entire project
Interacting with the CLI
- You will be working in a terminal environment where you have access to the dbt CLI, and potentially the dbt MCP server. The MCP server may include access to the dbt Cloud platform's APIs if relevant.
- You should prefer working with the dbt MCP server's tools, and help the user install and onboard the MCP when appropriate.
Common Mistakes
| Mistake |
Why It's Wrong |
Fix |
| One-shotting models |
Data work requires validation; schemas are unknown |
Follow planning-dbt-models.md, iterate with dbt show |
| Not working iteratively |
Changes to multiple models at once makes it hard to debug |
Run dbt build --select changed_model on each model after modifying it |
| Assuming schema knowledge |
Column names, types, and values vary across warehouses |
Follow discovering-data.md before writing SQL |
| Not reading existing model documentation |
Column names don't reveal business meaning |
Read YAML descriptions before modifying models |
| Creating unnecessary models |
Warehouse compute has real costs |
Extend existing models when possible |
| Hardcoding table names |
Breaks dbt's dependency graph |
Always use {{ ref() }} and {{ source() }} |
| Global config changes |
Configuration cascades unexpectedly |
Change surgically, match existing patterns |
| Running DDL directly |
Bypasses dbt's abstraction and tracking |
Use dbt commands exclusively |
Rationalizations to Resist
| Excuse |
Reality |
| "User explicitly asked for a new model" |
Users request out of habit. Ask why before complying. |
| "I've done this pattern hundreds of times" |
This project's schema may differ. Verify with dbt show. |
| "User is senior / knows what they're doing" |
Seniority doesn't change compute costs. Surface tradeoffs. |
| "It's just a small change" |
Small changes compound. Follow DRY principles. |
Red Flags - STOP and Reconsider
- About to write SQL without checking actual column names
- Modifying a model without reading its YAML documentation first
- Creating a new model when a column addition would suffice
- User gave table names as "the usual columns" - verify anyway
- Skipping
dbt show validation because "it's straightforward"
- Running DDL or queries directly against the warehouse
1---2name: using-dbt-for-analytics-engineering3description: Use when doing any dbt work - building or modifying models, debugging errors, exploring unfamiliar data sources, writing tests, or evaluating impact of changes. Use for analytics pipelines, data transformations, and data modeling.4---56# Using dbt for Analytics Engineering78**When you use this skill, announce:** "Using dbt analytics engineering skill to [brief purpose]."910**Core principle:** Apply software engineering discipline (DRY, modularity, testing) to data transformation work through dbt's abstraction layer.1112## When to Use1314- Building new dbt models, sources, or tests15- Modifying existing model logic or configurations16- Refactoring a dbt project structure17- Creating analytics pipelines or data transformations18- Working with warehouse data that needs modeling1920**Do NOT use for:**2122- Querying the semantic layer (use the `answering-natural-language-questions-with-dbt` skill)2324## Reference Guides2526This skill includes detailed reference guides for specific techniques. Read the relevant guide when needed:2728| Guide | Use When |29|-------|----------|30| `planning-dbt-models.md` | Building new models - work backwards from desired output and use `dbt show` to validate results |31| `discovering-data.md` | Exploring unfamiliar sources or onboarding to a project |32| `writing-data-tests.md` | Adding tests - prioritize high-value tests over exhaustive coverage |33| `debugging-dbt-errors.md` | Fixing project parsing, compilation, or database errors |34| `evaluating-impact-of-a-dbt-model-change.md` | Assessing downstream effects before modifying models |35| `writing-documentation.md` | Write documentation that doesn't just restate the column name |3637## DAG building guidelines3839- Conform to the existing style of a project (medallion layers, stage/intermediate/mart, etc)40- Focus heavily on DRY principles.41 - Before adding a new model or column, always be sure that the same logic isn't already defined elsewhere that can be used.42 - Prefer a change that requires you to add one column to an existing intermediate model over adding an entire additional model to the project.4344**When users request new models:** Always ask "why a new model vs extending existing?" before proceeding. Legitimate reasons exist (different grain, precalculation for performance), but users often request new models out of habit. Your job is to surface the tradeoff, not blindly comply.4546## Model building guidelines4748- Always use data modelling best practices when working in a project49- Write dbtonic code:50 - Always use `{{ ref }}` and `{{ source }}` over hardcoded table names51 - Use CTEs over subqueries52- Before building a model, follow `planning-dbt-models.md` to plan your approach.53- Before modifying or building on existing models, read their YAML documentation:54 - Find the model's YAML file (can be any `.yml` or `.yaml` file in the models directory, but normally colocated with the SQL file)55 - Check the model's `description` to understand its purpose56 - Read column-level `description` fields to understand what each column represents57 - Review any `meta` properties that document business logic or ownership58 - This context prevents misusing columns or duplicating existing logic5960## You must look at the data to be able to correctly model the data6162When implementing a model, you must use `dbt show` regularly to:63 - preview the input data you will work with, so that you use relevant columns and values64 - preview the results of your model, so that you know your work is correct65 - run basic data profiling (counts, min, max, nulls) of input and output data, to check for misconfigured joins or other logic errors6667## Cost management best practices6869- Use `--limit` with `dbt show` and insert limits early into CTEs when exploring data70- Use deferral (`--defer --state path/to/prod/artifacts`) to reuse production objects71- Use [`dbt clone`](https://docs.getdbt.com/reference/commands/clone) to produce zero-copy clones72- Avoid large unpartitioned table scans in BigQuery73- Always use `--select` instead of running the entire project7475## Interacting with the CLI7677- You will be working in a terminal environment where you have access to the dbt CLI, and potentially the dbt MCP server. The MCP server may include access to the dbt Cloud platform's APIs if relevant.78- You should prefer working with the dbt MCP server's tools, and help the user install and onboard the MCP when appropriate.7980## Common Mistakes8182| Mistake | Why It's Wrong | Fix |83|---------|----------------|-----|84| One-shotting models | Data work requires validation; schemas are unknown | Follow `planning-dbt-models.md`, iterate with `dbt show` |85| Not working iteratively | Changes to multiple models at once makes it hard to debug | Run `dbt build --select changed_model` on each model after modifying it |86| Assuming schema knowledge | Column names, types, and values vary across warehouses | Follow `discovering-data.md` before writing SQL |87| Not reading existing model documentation | Column names don't reveal business meaning | Read YAML descriptions before modifying models |88| Creating unnecessary models | Warehouse compute has real costs | Extend existing models when possible |89| Hardcoding table names | Breaks dbt's dependency graph | Always use `{{ ref() }}` and `{{ source() }}` |90| Global config changes | Configuration cascades unexpectedly | Change surgically, match existing patterns |91| Running DDL directly | Bypasses dbt's abstraction and tracking | Use dbt commands exclusively |9293## Rationalizations to Resist9495| Excuse | Reality |96|--------|---------|97| "User explicitly asked for a new model" | Users request out of habit. Ask why before complying. |98| "I've done this pattern hundreds of times" | This project's schema may differ. Verify with `dbt show`. |99| "User is senior / knows what they're doing" | Seniority doesn't change compute costs. Surface tradeoffs. |100| "It's just a small change" | Small changes compound. Follow DRY principles. |101102## Red Flags - STOP and Reconsider103104- About to write SQL without checking actual column names105- Modifying a model without reading its YAML documentation first106- Creating a new model when a column addition would suffice107- User gave table names as "the usual columns" - verify anyway108- Skipping `dbt show` validation because "it's straightforward"109- Running DDL or queries directly against the warehouse
tools-only