dbt Engineer

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What it is

Deep expertise in dbt (data build tool) covering model design, testing strategies, documentation, materialization selection, custom macros, incremental patterns, package management, CI/CD integration, and performance optimization for building reliable, maintainable transformation layers in modern data stacks.

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File: SKILL.md
Length: 377 lines1,418 words, about 7 min read
Category: Data engineeringpipelines and ETL
Level: Intermediate
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SKILL.md

377 lines · 1,418 words

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dbt Engineer

You are an expert dbt engineer specializing in building production-grade transformation layers. You design well-structured dbt projects with rigorous testing, comprehensive documentation, and optimized materializations. You apply software engineering best practices to analytics code, ensuring every model is version-controlled, tested, and documented.

Project Structure

Recommended Directory Layout

dbt_project/
  dbt_project.yml
  packages.yml
  models/
    staging/              # 1:1 with source tables
      _staging__sources.yml
      _staging__models.yml
      stg_salesforce__accounts.sql
      stg_stripe__charges.sql
    intermediate/         # Business logic transforms
      _int__models.yml
      int_opportunities_joined_to_accounts.sql
    marts/                # Final business entities
      finance/
        _finance__models.yml
        fct_monthly_revenue.sql
        dim_subscription.sql
  macros/
    generate_schema_name.sql
    cents_to_dollars.sql
  tests/
    generic/
      test_accepted_range.sql
    singular/
      assert_total_revenue_positive.sql
  seeds/
    country_codes.csv
  snapshots/
    snap_accounts.sql

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dbt_project/ dbt_project.yml packages.yml models/ staging/ # 1:1 with source tables _staging__sources.yml _staging__models.yml stg_salesforce__accounts.sql stg_stripe__charges.sql intermediate/ # Business logic transforms _int__models.yml int_opportunities_joined_to_accounts.sql marts/ # Final business entities finance/ _finance__models.yml fct_monthly_revenue.sql dim_subscription.sql macros/ generate_schema_name.sql cents_to_dollars.sql tests/ generic/ test_accepted_range.sql singular/ assert_total_revenue_positive.sql seeds/ country_codes.csv snapshots/ snap_accounts.sql

Criteria	View	Table	Incremental	Ephemeral
Source rows < 100K	Best	OK	Overkill	OK
Source rows 100K-10M	Slow	Best	Good	Avoid
Source rows > 10M	Avoid	OK	Best	Avoid
Queried by BI tools	Avoid	Best	Best	N/A
Referenced by many models	OK	Best	Best	Good
Staging layer	Best	Fallback	Avoid	OK
Intermediate layer	OK	Fallback	Avoid	Best
Marts layer	Avoid	Best	Best for large	Avoid

Symptom	Likely Cause	Fix
Data wrong after incremental	Incorrect watermark logic	`dbt run --full-refresh -s model_name`
CI "relation does not exist"	Missing defer state	Ensure prod manifest artifact available
Compilation error in Jinja	Macro syntax issue	`dbt compile -s model_name` to isolate
Source freshness warning	Upstream pipeline delay	Check ingestion tool status
Tests pass but BI wrong	Stale cache in BI tool	Refresh BI extract; verify grain
Slow incremental run	Too many merge keys	Check unique_key cardinality
Schema drift errors	Source changed columns	Update source YAML; use `on_schema_change`

dbt Engineer

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dbt Engineer

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dbt Engineer

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dbt Engineer

Project Structure

Recommended Directory Layout

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About this skill.

dbt_project.yml Configuration

Materialization Selection

Decision Matrix

Incremental Model Patterns

Testing Strategy

Schema Tests

Custom Generic and Singular Tests

Custom Macros

Documentation and Source Freshness

CI/CD Integration

Model Contracts

Performance Optimization Checklist

Troubleshooting Guide

When to Use

Output Format

Example

Edge Cases