---
name: analytics-engineer
description: >
  Expert analytics engineering covering data modeling, dbt development, data
  transformation, and semantic layer management. Use when building dbt models,
  designing star schemas, writing staging or mart SQL, configuring data tests,
  defining semantic-layer metrics, or optimizing warehouse query performance.
license: MIT + Commons Clause
metadata:
  version: 1.0.0
  author: borghei
  category: data-analytics
  updated: 2026-03-31
  tags:
    - analytics-engineering
    - dbt
    - data-modeling
    - transformation
    - semantic-layer
---
# 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.

## 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.