analytics-engineer

name	analytics-engineer
description	Analytics engineering across data modeling, dbt, transformation, and semantic layers. Use when building dbt models, designing star schemas, writing staging or mart SQL, configuring data tests, or optimizing warehouse queries.
license	MIT + Commons Clause
metadata	{"version":"1.0.0","author":"borghei","category":"data-analytics","updated":"2026-03-31T00:00:00.000Z","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.

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):

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):

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  customer_orders co
         c.customer_id  co.customer_id
)

   

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

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

-- 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 

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

# 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

-- 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

# 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

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.