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., int_orders_enriched). 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 + not_null. Foreign keys get relationships. Add accepted_values for enums. Write model descriptions in YAML.
7. Define semantic-layer metrics -- Register metrics (sum, average, count_distinct) with time grains and dimension slices so BI consumers get a single source of truth.
8. Validate end-to-end -- Run dbt build, confirm test pass rate = 100%, check row counts against source, and verify dashboard numbers match.

dbt Project Structure

analytics/
  dbt_project.yml
  models/
    staging/          # stg_<source>__<table>.sql  (one per source table)
    intermediate/     # int_<entity>_<verb>.sql     (reusable logic)
    marts/
      core/           # dim_*.sql, fct_*.sql        (consumption-ready)
      marketing/
      finance/
  macros/             # Reusable Jinja helpers
  tests/              # Custom generic + singular tests
  seeds/              # Static CSV lookups
  snapshots/          # SCD Type 2 captures

Concrete Example: Customer Dimension

Staging model (models/staging/crm/stg_crm__customers.sql):

sql
WITH source AS (
    SELECT * FROM {{ source('crm', 'customers') }}
),

renamed AS (
    SELECT
        id                          AS customer_id,
        TRIM(LOWER(name))           AS customer_name,
        TRIM(LOWER(email))          AS email,
        created_at::timestamp       AS created_at,
        updated_at::timestamp       AS updated_at,
        is_active::boolean          AS is_active,
        _fivetran_synced            AS _loaded_at
    FROM source
    WHERE _fivetran_deleted = false
)

SELECT * FROM renamed

Mart model (models/marts/core/dim_customer.sql):

sql
WITH customers AS (
    SELECT * FROM {{ ref('stg_crm__customers') }}
),

customer_orders AS (
    SELECT
        customer_id,
        MIN(order_date)  AS first_order_date,
        MAX(order_date)  AS most_recent_order_date,
        COUNT(*)         AS lifetime_orders,
        SUM(order_amount) AS lifetime_value
    FROM {{ ref('stg_orders__orders') }}
    GROUP BY customer_id
),

final AS (
    SELECT
        c.customer_id,
        c.customer_name,
        c.email,
        c.created_at,
        co.first_order_date,
        co.most_recent_order_date,
        co.lifetime_orders,
        co.lifetime_value,
        CASE
            WHEN co.lifetime_value >= 10000 THEN 'platinum'
            WHEN co.lifetime_value >= 5000  THEN 'gold'
            WHEN co.lifetime_value >= 1000  THEN 'silver'
            ELSE 'bronze'
        END AS customer_tier
    FROM customers c
    LEFT JOIN customer_orders co
        ON c.customer_id = co.customer_id
)

SELECT * FROM final

Test configuration (models/marts/core/_core__models.yml):

yaml
version: 2
models:
  - name: dim_customer
    description: Customer dimension with lifetime order metrics and tier classification.
    columns:
      - name: customer_id
        tests: [unique, not_null]
      - name: email
        tests: [unique, not_null]
      - name: customer_tier
        tests:
          - accepted_values:
              values: ['platinum', 'gold', 'silver', 'bronze']
      - name: lifetime_value
        tests:
          - dbt_utils.expression_is_true:
              expression: ">= 0"

Incremental Fact Table Pattern

sql
-- models/marts/core/fct_orders.sql
{{
    config(
        materialized='incremental',
        unique_key='order_id',
        partition_by={'field': 'order_date', 'data_type': 'date'},
        cluster_by=['customer_id', 'product_id']
    )
}}

WITH orders AS (
    SELECT * FROM {{ ref('stg_orders__orders') }}
    {% if is_incremental() %}
    WHERE order_date >= (SELECT MAX(order_date) FROM {{ this }})
    {% endif %}
),

order_items AS (
    SELECT * FROM {{ ref('stg_orders__order_items') }}
),

final AS (
    SELECT
        o.order_id,
        o.order_date,
        o.customer_id,
        oi.product_id,
        o.store_id,
        oi.quantity,
        oi.unit_price,
        oi.quantity * oi.unit_price AS line_total,
        o.discount_amount,
        o.tax_amount,
        o.total_amount
    FROM orders o
    INNER JOIN order_items oi ON o.order_id = oi.order_id
)

SELECT * FROM final

Materialization Strategy

| Layer | Materialization | Rationale | |-------|----------------|-----------| | Staging | View | Thin wrappers; no storage cost | | Intermediate | Ephemeral / View | Business logic; referenced multiple times | | Marts (small) | Table | Query performance for BI tools | | Marts (large) | Incremental | Efficient appends for large fact tables |

Semantic-Layer Metric Definition

yaml
# models/marts/core/_core__metrics.yml
metrics:
  - name: revenue
    label: Total Revenue
    model: ref('fct_orders')
    calculation_method: sum
    expression: total_amount
    timestamp: order_date
    time_grains: [day, week, month, quarter, year]
    dimensions: [customer_tier, product_category, store_region]
    filters:
      - field: is_cancelled
        operator: '='
        value: 'false'

  - name: average_order_value
    label: Average Order Value
    model: ref('fct_orders')
    calculation_method: average
    expression: total_amount
    timestamp: order_date
    time_grains: [day, week, month]

Useful Macros

sql
-- macros/cents_to_dollars.sql
{% macro cents_to_dollars(column_name) %}
    ({{ column_name }} / 100.0)::decimal(18,2)
{% endmacro %}

-- macros/get_incremental_filter.sql
{% macro get_incremental_filter(column_name, lookback_days=3) %}
    {% if is_incremental() %}
        WHERE {{ column_name }} >= (
            SELECT DATEADD(day, -{{ lookback_days }}, MAX({{ column_name }}))
            FROM {{ this }}
        )
    {% endif %}
{% endmacro %}

CI/CD: Slim CI for Pull Requests

bash
# Only run modified models and their downstream dependents
dbt run  --select state:modified+ --defer --state ./target-base
dbt test --select state:modified+ --defer --state ./target-base

For full CI/CD pipeline configuration, see REFERENCE.md.

Reference Materials

• REFERENCE.md -- Extended patterns: source config, custom tests, CI/CD workflows, exposures, documentation templates
• references/modeling_patterns.md -- Data modeling best practices
• references/dbt_style_guide.md -- SQL and dbt conventions
• references/testing_guide.md -- Testing strategies
• references/optimization.md -- Performance tuning

Scripts

bash
python scripts/impact_analyzer.py --model dim_customer
python scripts/schema_diff.py --source prod --target dev
python scripts/doc_generator.py --format markdown
python scripts/quality_scorer.py --model fct_orders

Tool Reference

| Tool | Purpose | Key Flags | |------|---------|-----------| | impact_analyzer.py | Trace downstream impact of a dbt model via BFS on the manifest DAG | --model <name>, --manifest <path>, --json | | schema_diff.py | Compare two dbt catalog.json files to detect column additions, removals, and type changes | --source <path>, --target <path>, --json | | doc_generator.py | Generate markdown documentation (column dictionary, dependencies, tests) for a dbt model | --model <name>, --manifest <path>, --catalog <path> | | quality_scorer.py | Score a dbt model 0-100 based on documentation, testing, and layer-convention adherence | --model <name>, --manifest <path>, --json |

Troubleshooting

| Problem | Likely Cause | Resolution | |---------|-------------|------------| | dbt build fails with "relation does not exist" | Upstream model was not run or materialization changed | Run dbt build --select +<model> to build the full upstream chain | | Incremental model produces duplicates | unique_key does not match the actual grain | Verify the unique_key config matches the primary key columns; run a full refresh with --full-refresh | | Test failures on not_null after deployment | Source data introduced unexpected NULLs in a previously clean column | Add a staging-layer COALESCE or adjust the test to warn severity while investigating upstream | | Schema drift detected by schema_diff.py | Upstream source changed column types or removed columns | Coordinate with the data engineering team; update staging model casts and regenerate documentation | | Semantic-layer metric values differ from dashboard | Dashboard applies its own filters or calculations outside the semantic layer | Move all calculation logic into the semantic layer; audit dashboard-level computed fields | | Slow dbt run on large incremental models | Lookback window is too wide or partition pruning is not engaged | Narrow the incremental filter, verify partition_by config, and check warehouse query plan | | quality_scorer.py reports low score despite good coverage | Staging model contains JOINs or GROUP BY operations triggering layer-violation penalties | Refactor aggregation logic into intermediate or mart models; keep staging models as thin wrappers |

Success Criteria

• All dbt models pass dbt build with a 100% test pass rate before merging to production.
• Every model has a YAML description and at least one test per primary key (unique + not_null).
• Incremental models process new data in under 5 minutes for tables up to 100M rows.
• Schema drift between prod and dev environments is detected and reviewed before each release.
• quality_scorer.py reports >= 80/100 for every mart model.
• Downstream dashboards refresh within SLA (< 5 s load time) after transformation runs complete.
• Semantic-layer metrics are the single source of truth -- no ad-hoc metric calculations exist in BI tools.

Scope & Limitations

In scope: dbt project design, dimensional modeling (Kimball methodology), SQL transformation logic, data testing, semantic-layer metric definition, CI/CD for dbt, and warehouse query optimization.

Out of scope: Raw data ingestion and extraction (ELT/ETL orchestration tools like Fivetran or Airbyte), data infrastructure provisioning, BI tool configuration beyond semantic-layer integration, and real-time streaming pipelines.

Limitations: The Python tools operate on dbt manifest/catalog JSON artifacts and do not query the warehouse directly. Scoring heuristics in quality_scorer.py use rule-based deductions that may not cover every project convention. All scripts use the Python standard library only -- no external dependencies required.

Integration Points

• Data Engineer (engineering/senior-data-engineer): Coordinates on source table contracts, ingestion SLAs, and schema change notifications.
• Business Intelligence (data-analytics/business-intelligence): Consumes mart models and semantic-layer metrics; dashboard specs reference model outputs.
• Data Analyst (data-analytics/data-analyst): Writes ad-hoc queries against mart models; reports data quality issues back to the analytics engineer.
• MLOps Engineer (data-analytics/ml-ops-engineer): Feature engineering pipelines may depend on intermediate or mart models as upstream inputs.
• CI/CD Workflows (templates/): Slim CI patterns (state:modified+) integrate into GitHub Actions or similar runners for automated PR validation.