lfx-data-engineer

name	lfx-data-engineer
description	Guide non-dbt developers through building PR-ready data models, tests, and transformations in the lf-dbt repo. Encodes the medallion architecture (bronze/silver/gold/platinum), Snowflake SQL conventions, sqlfluff formatting, dbt testing patterns, key macros, and data governance rules. Use this skill any time someone asks about writing dbt models, adding data tests, creating SQL transformations, fixing pipeline failures, or contributing to the lf-dbt repository.
allowed-tools	Bash, Read, Write, Edit, Glob, Grep, AskUserQuestion

LFX Data Engineering

You are generating dbt models and SQL transformations that must be PR-ready. This skill encodes all conventions for the lf-dbt repository, which implements a medallion architecture data warehouse on Snowflake.

Prerequisites: Snowflake access must be provisioned first (via /lfx-snowflake-access).

Input Validation

Before generating any code, verify your args include:

Required	If Missing
Specific task (what to build/modify)	Stop and ask — do not guess
Which medallion layer (bronze/silver/gold/platinum)	Infer from task, but confirm
Data source name (for bronze) or upstream model (for silver+)	Stop and ask — never assume
Target file path(s)	Infer from naming conventions, but verify they exist
Example pattern to follow	Find one yourself (see Read Before Generating)

If invoked with a FIX: prefix, this is an error correction. Read the error, find the file, apply the targeted fix, and re-validate.

Read Before Generating — MANDATORY

Before writing ANY code, you MUST:

1. Read the target file (if modifying) — understand what's already there
2. Read one example file in the same layer and domain — match the exact patterns
3. Read the relevant YML test file — ensure your model will be tested consistently

Do NOT generate code from memory alone. The codebase may have evolved since your training data.

# Example: before creating a new bronze model, read an existing one in the same source
cat models/bronze/fivetran_platform/bronze_fivetran_platform_events.sql
# And read the test file
cat models/bronze/fivetran_platform/bronze_fivetran_platform_tests.yml

License Header

Every new .sql file MUST start with this header:

-- Copyright The Linux Foundation and each contributor to LFX.
-- SPDX-License-Identifier: MIT

Every new .yml file MUST start with:

# Copyright The Linux Foundation and each contributor to LFX.
# SPDX-License-Identifier: MIT

Completion Report

When you finish, output a clear summary:

═══════════════════════════════════════════
/lfx-data-engineer COMPLETE
═══════════════════════════════════════════
Files created:
  - models/bronze/fivetran_platform/bronze_fivetran_platform_new_table.sql

Files modified:
  - models/bronze/fivetran_platform/bronze_fivetran_platform_tests.yml — added new_table tests

Validation:
  - Ran: sqlfluff lint models/bronze/fivetran_platform/bronze_fivetran_platform_new_table.sql
  - Result: ✓ passed / ✗ failed with: <error>
  - Ran: dbt compile --select bronze_fivetran_platform_new_table
  - Result: ✓ passed / ✗ failed with: <error>

Notes:
  - Source table 'new_table' must exist in the fivetran_platform source definition

Errors:
  - (none)
═══════════════════════════════════════════

Always include the Validation section. Run sqlfluff lint and dbt compile after creating or modifying files. Report the result.

---

Medallion Architecture Quick Reference

Layer	Materialization	Schema	Purpose
Bronze	view (default)	bronze_* (per source)	1:1 with source data — column renames, type casting, filter deletes/test data
Silver	table	silver_dim, silver_fact	Business logic, joins, reusable business objects
Gold	table	gold_* (per domain)	Aggregated metrics for specific business use cases
Platinum	table	platinum* (per product)	Pre-computed reports with time windows for dashboards

References

Task	Reference
Environment setup, dbt commands, clone workflow	references/getting-started.md
Detailed layer guide with SQL examples and decision tree	references/medallion-architecture.md
SQL formatting, keyword casing, indentation, CTEs, JOINs	references/sql-style-guide.md
dbt test conventions, PII tagging, primary key tests	references/testing-patterns.md
Project macros: smart_source, format_timestamp, date ranges, deltas	references/key-macros.md
Troubleshooting build failures, sqlfluff, incremental issues	references/debugging-pipelines.md

---

Creating a Model by Layer

Bronze — Source Extraction

Bronze models are 1:1 with source tables. They rename columns, cast types, and filter out deleted/test records. No business logic.

-- Copyright The Linux Foundation and each contributor to LFX.
-- SPDX-License-Identifier: MIT

SELECT
    id AS event_id,
    event_title AS event_name,
    event_start_date,
    event_end_date,
    created_date AS event_created_ts,
    lastmodified_date AS updated_at

FROM {{ source('fivetran_platform', 'event') }}
WHERE
    NOT _fivetran_deleted
    AND NOT is_test

Bronze rules:

• Use source() to reference raw tables (or smart_source() for dev lookback)
• Rename columns to snake_case with business-friendly names
• Timestamps: suffix _ts; Dates: suffix _date; Booleans: prefix is_ or has_
• Filter _fivetran_deleted and test data rows
• No JOINs — one source table per model
• Use get_warehouse('hourly') in config if the source is large

Silver — Business Logic

Silver models join bronze models, apply business rules, and create reusable objects. Split into dim/ (dimensions) and fact/ (facts).

-- Copyright The Linux Foundation and each contributor to LFX.
-- SPDX-License-Identifier: MIT

{% set warehouse = get_warehouse('hourly') %}

{{ config(snowflake_warehouse=warehouse) }}

/*
Purpose:
    Create a reusable project dimension with core Salesforce project attributes
    and the latest project health score for downstream analytics.

Questions answered:
    - What are the canonical identifiers and names for each project?
    - What is the current health score associated with each project?

Data sources:
    - bronze_fivetran_salesforce_projects
    - silver_fact_crowd_dev_project_health_metrics
*/

WITH source_data AS (
    SELECT
        project_id,
        project_name,
        project_slug,
        project_status
    FROM {{ ref('bronze_fivetran_salesforce_projects') }}
),

enriched AS (
    SELECT
        s.project_id,
        s.project_name,
        s.project_slug,
        s.project_status,
        h.health_score
    FROM source_data s
    LEFT JOIN {{ ref('silver_fact_crowd_dev_project_health_metrics') }} h
        ON s.project_slug = h.project_slug
)

SELECT
    project_id,
    project_name,
    project_slug,
    project_status,
    health_score
FROM enriched

Silver rules:

• Use ref() to reference bronze or other silver models
• CTEs for each logical step (one unit of work per CTE)
• Verbose CTE names that describe what they do
• Include a block comment at the top explaining purpose, questions answered, and data sources
• dim/ for slowly-changing attributes; fact/ for events and transactions

Gold — Aggregated Metrics

Gold models combine silver models into purpose-built datasets for specific use cases.

-- Copyright The Linux Foundation and each contributor to LFX.
-- SPDX-License-Identifier: MIT

{{ config(unique_key=["_key", "project_id"]) }}

SELECT
    ({{ dbt_utils.generate_surrogate_key(["c._key", "p.mapped_project_id"]) }}) AS activity_project_id,
    c._key,
    c.activity_id,
    c.activity_ts,
    p.mapped_project_id AS project_id,
    p.mapped_project_slug AS project_slug

FROM {{ ref("silver_fact_crowd_dev_activities") }} c
LEFT JOIN {{ ref("_silver_dim_project_spine") }} p
    ON c.project_id = p.base_project_id
WHERE
    p.mapped_project_id IS NOT NULL
    AND {{ filter_code_contributions_non_bot('c') }}

Gold rules:

• Use dbt_utils.generate_surrogate_key() for composite primary keys
• Always specify unique_key in config for incremental models
• Reference silver models via ref(), apply domain-specific macros
• Final SELECT should explicitly list all columns — no SELECT *

Platinum — Pre-Computed Reports

Platinum models produce dashboard-ready data with time-windowed aggregations.

-- Copyright The Linux Foundation and each contributor to LFX.
-- SPDX-License-Identifier: MIT

{% set warehouse = get_warehouse('hourly') %}

{{ config(snowflake_warehouse=warehouse) }}

WITH base AS (
    SELECT
        user_id,
        event_id,
        event_name,
        event_start_date
    FROM {{ ref('silver_fact_event_registrations') }}
    WHERE event_name IS NOT NULL
)

SELECT
    ({{ dbt_utils.generate_surrogate_key(['user_id', 'event_id']) }}) AS _key,
    user_id,
    event_id,
    event_name,
    event_start_date
FROM base
QUALIFY ROW_NUMBER() OVER (
    PARTITION BY user_id, event_id
    ORDER BY event_start_date
) = 1

Platinum rules:

• Use date range macros (is_last_30_days, is_year_to_date, etc.) for time windows
• Use get_warehouse() for resource-intensive models
• GROUP BY ALL is acceptable for complex aggregations
• QUALIFY with ROW_NUMBER() for deduplication
• Purpose-built for specific dashboards (PCC, Individual Dashboard, Org Dashboard)

---

Writing Tests (YML)

Every model needs a corresponding entry in a *_tests.yml file. Use data_tests: (not the deprecated tests:). Parameterized tests require the arguments: wrapper.

# Copyright The Linux Foundation and each contributor to LFX.
# SPDX-License-Identifier: MIT

version: 2
models:
  - name: my_new_model
    description: "What this model contains and its purpose."
    columns:
      - name: _key
        description: "The unique primary key for the table."
        data_tests:
          - unique
          - not_null
          - dbt_utils.not_empty_string

      - name: status
        description: "The current status."
        data_type: string
        data_tests:
          - not_null
          - accepted_values:
              arguments:
                values: ["active", "inactive", "pending"]

      - name: project_id
        description: "Foreign key to the projects dimension."
        data_type: string
        data_tests:
          - not_null
          - relationships:
              arguments:
                to: ref('silver_dim_projects')
                field: project_id

      - name: email
        description: "User email address"
        data_type: string
        config:
          meta:
            contains_pii: true
            data_retention: "undefined"

See references/testing-patterns.md for full conventions.

---

SQL Style Rules (Summary)

Rule	Example
Uppercase SQL keywords	SELECT, FROM, WHERE, LEFT JOIN
Lowercase identifiers	event_id, project_name
4-space indentation	Indent columns under SELECT, conditions under WHERE
Trailing commas	event_id, (not , event_id)
CTEs over subqueries	Use WITH ... AS (...) instead of nested SELECT
Default to INNER JOIN	Use LEFT JOIN only when right side may have no matches
No RIGHT JOIN	Rewrite as LEFT JOIN
No SELECT DISTINCT	Requires architect approval
GROUP BY by number	GROUP BY 1, 2 preferred over column names
Explicit column lists	No SELECT * in final SELECT
Pre-filter in CTEs	Complex filtering on joined tables belongs in a CTE before the join

See references/sql-style-guide.md for full formatting rules.

---

Key Macros

Macro	Purpose	When to Use
smart_source()	Dev-friendly source wrapper with lookback	Bronze models reading from source tables
format_timestamp()	Generate UTC _ts and local _ts_local columns	Bronze models normalizing timestamps
to_utc_timestamp()	Convert local timestamp to UTC with dynamic timezone	When timezone is a column, not a constant
get_warehouse()	Select warehouse by size (default, hourly, medium)	Large models needing specific compute
generate_alias_name	Strips schema prefix from table name (e.g., silver_dim_ → table name)	Automatic — configured in macros
is_last_7_days(), is_last_30_days(), etc.	Date range filters for time windows	Platinum models with pre-computed periods
is_prev_7_days(), is_prev_30_days(), etc.	Previous period for period-over-period comparison	Delta/change calculations
add_delta_columns()	Generate _prev, _diff, _delta columns	Period-over-period metric comparisons
get_month(), get_quarter()	Human-readable date labels	Display-friendly date columns
gdpr_filter_email()	Exclude GDPR-suppressed emails	Any model exposing email addresses
filter_code_contributions_non_bot()	Exclude bot code contributions	Code contribution models
format_country()	Normalize country names to canonical values	Models with user-entered country data
comprehensive_email_filter()	Validate email format + exclude test emails	Email-based models

See references/key-macros.md for full documentation and usage examples.

---

Data Governance

PII Tagging

Columns containing personally identifiable information (names, emails, addresses, etc.) must be tagged in the YML file. Use config.meta — not top-level meta.

columns:
  - name: email
    description: "User email address"
    config:
      meta:
        contains_pii: true
        data_retention: "undefined"

Timestamp Normalization

All timestamps must be normalized to UTC in the bronze layer:

• Timestamps: _ts suffix, stored as TIMESTAMP_NTZ in UTC
• Dates: _date suffix, stored as DATE
• Use format_timestamp() macro for consistent conversion
• Use convert_timezone() for explicit timezone conversion

Primary Key Convention

• Use _key suffix for primary key columns
• Always add unique, not_null, and not_empty_string tests

---

Common Anti-Patterns — DO NOT DO THESE

Anti-Pattern	Correct Pattern
Missing license header	Always add -- Copyright The Linux Foundation...
tests: in YML	Use data_tests: (dbt v1.10.5+)
meta: at top level in YML	Nest under config: → meta:
Missing arguments: on parameterized tests	accepted_values: → arguments: → values:
tags: at top level in YML	Nest under config: → tags:
Duplicate config: keys in YML	Combine into a single config: block
Custom keys directly in config:	Nest under config: → meta:
SELECT DISTINCT	Use GROUP BY or QUALIFY ROW_NUMBER()
RIGHT JOIN	Rewrite as LEFT JOIN
Filtering right side of LEFT JOIN in WHERE	Filter in the ON clause or in a CTE
SELECT * in final select	Explicitly list all columns
Subqueries in FROM or JOIN	Use CTEs
Raw source() in dev (large tables)	Use smart_source() with lookback
Hardcoded warehouse name	Use get_warehouse() macro
console.log / print debugging	Use dbt compile and dbt show
Committing without --signoff or -S	Always use signed commits with DCO

---

Pre-PR Checklist

All Models

• License header on all new .sql and .yml files
• Model documented in corresponding *_tests.yml file
• Primary key column(s) have unique, not_null, dbt_utils.not_empty_string tests
• PII columns tagged with config.meta.contains_pii: true and data_retention: "undefined"
• sqlfluff lint passes on all new/modified .sql files
• dbt compile --select +model_name succeeds
• Column naming follows conventions (_ts, _date, is_, has_, _key)
• No SELECT * in final select statements
• All timestamps normalized to UTC

Bronze Models

• 1:1 with source table — no joins
• Filters _fivetran_deleted and test data
• Column renames to snake_case with business-friendly names
• Uses source() or smart_source()

Silver Models

• Uses ref() to reference upstream models
• CTEs for each logical unit of work
• Block comment explaining purpose and data sources
• Placed in correct subfolder (dim/ or fact/)

Gold Models

• Surrogate key generated for composite keys
• unique_key specified in config for incremental models
• Final SELECT explicitly lists all columns

Platinum Models

• Uses date range macros for time windows
• get_warehouse() configured if resource-intensive
• Purpose-built for a specific dashboard or use case

---

Scope Boundaries

This skill DOES:

• Generate/modify dbt SQL models following medallion architecture
• Create/update YML test files with proper data_tests format
• Add source definitions for new data sources
• Apply project macros (smart_source, format_timestamp, date ranges, etc.)
• Run sqlfluff lint/fix validation after changes
• Run dbt compile to verify model correctness

This skill does NOT:

• Run dbt build/test against the warehouse (use the running-dbt-commands skill)
• Modify existing macros without architect review
• Make architectural decisions about layer placement (ask the user)
• Generate semantic layer definitions (use the building-dbt-semantic-layer skill)
• Troubleshoot dbt Cloud job failures (use the troubleshooting-dbt-job-errors skill)
• Modify protected infrastructure files (dbt_project.yml, profiles.yml, packages.yml) — flag for code owner