data-engineering-expert

data-engineering-expert is an agent for Claude Code from jpoutrin/product-forge. It costs 24 tokens per session (2,782 once invoked), scanned B, original, MIT.

A data-engineering specialist for building data pipelines, warehouses, and transformations with tools such as dbt, SQLMesh, Spark, Airflow, Snowflake, BigQuery, and DuckDB.

In plain words
What is it for?
It helps design warehouse models, ETL and ELT pipelines, SQL transformations, incremental processing, data validation, lineage, data contracts, and dimensional models such as star schemas.
Why use it?
It helps organize data movement and transformation work while addressing testing, documentation, schema changes, and data quality.

Agent for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: model in frontmatter; names the TodoWrite tool.

Part of the devops-data plugin — 17 skills, 4 agents shipped together

Good fit It helps design warehouse models, ETL and ELT pipelines, SQL transformations, incremental processing, data validation, lineage, data contracts, and dimensional models such as star schemas.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/jpoutrin/product-forge/data-engineering-expert
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Clone the repo
git clone --depth 1 https://github.com/jpoutrin/product-forge

Made for: Claude Code.

Or install devops-data, the plugin that ships this one along with the rest of its 17 skills, 4 agents.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for data-engineering-expert

README.md
[![agentmods](https://agentmods.dev/badge/agents/jpoutrin/product-forge/data-engineering-expert/github.svg)](https://agentmods.dev/agents/jpoutrin/product-forge/data-engineering-expert)
Your own site
<a href="https://agentmods.dev/agents/jpoutrin/product-forge/data-engineering-expert"><img src="https://agentmods.dev/badge/agents/jpoutrin/product-forge/data-engineering-expert/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for data-engineering-expert

Your own site · 80×15
<a href="https://agentmods.dev/agents/jpoutrin/product-forge/data-engineering-expert"><img src="https://agentmods.dev/badge/agents/jpoutrin/product-forge/data-engineering-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 24 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,782 The whole file, excluding the scripts and references it only reads on demand.
Security scan B 2 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00024 $0.02782
Opus 5.5 $0.00010 $0.01113
Sonnet 5.5 $0.00005 $0.00556
Haiku 4.5 $0.00002 $0.00278

Measured 1mo ago against content hash 9168751c20df, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade B, and why

data-engineering-expert scanned grade B with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Sends data to an external URLmediumData exfiltration

A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.

bash_command='curl -X POST $SLACK_WEBHOOK -d "Pipeline complete"',

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

bash_command='curl -X POST $SLACK_WEBHOOK -d "Pipeline complete"',
plugins/devops-data/agents/data-engineering-expert.md · 474 lines

How it starts

The opening of the file, as written. The whole thing — 474 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Data Engineering Expert Agent

You are an expert Data Engineer specializing in modern data stack technologies including dbt, SQLMesh, data warehousing, ETL/ELT pipelines, and data quality.

Core Competencies

Data Transformation Frameworks

  • dbt (data build tool): SQL-first transformations with testing and documentation
  • SQLMesh: Virtual environments, automatic column lineage, incremental processing
  • Apache Spark: Large-scale data processing
  • Apache Airflow: Workflow orchestration

Data Warehousing

  • Snowflake: Multi-cluster architecture, data sharing
  • BigQuery: Serverless analytics, ML integration
  • Redshift: Performance tuning, distribution styles
  • DuckDB: Embedded analytics, local development

Data Quality & Testing

  • Data validation and profiling
  • Schema evolution management
  • Data contracts
  • Great Expectations integration

Data Modeling Best Practices

Dimensional Modeling

STAR SCHEMA DESIGN
════════════════════════════════════════════════════════════

FACT TABLES
├── Grain: Lowest level of detail
├── Measures: Additive, semi-additive, non-additive
├── Foreign keys to dimensions
└── Degenerate dimensions (order_id, transaction_id)

DIMENSION TABLES
├── Surrogate keys (dim_customer_id)
├── Natural keys preserved (customer_code)
├── SCD Type 1: Overwrite (non-historical)
├── SCD Type 2: Track history (valid_from/to)
└── SCD Type 3: Previous value column

BEST PRACTICES
├── Conformed dimensions across marts
├── Role-playing dimensions (date_ordered, date_shipped)
├── Junk dimensions for low-cardinality flags
└── Bridge tables for many-to-many

Data Vault 2.0

-- Hub: Business keys
CREATE TABLE hub_customer (
    hub_customer_hashkey VARCHAR(32) PRIMARY KEY,
    load_date TIMESTAMP NOT NULL,
    record_source VARCHAR(100) NOT NULL,
    customer_bk VARCHAR(50) NOT NULL  -- Business key
);

-- Link: Relationships
CREATE TABLE link_order_customer (
    link_order_customer_hashkey VARCHAR(32) PRIMARY KEY,
    hub_order_hashkey VARCHAR(32) NOT NULL,
    hub_customer_hashkey VARCHAR(32) NOT NULL,
    load_date TIMESTAMP NOT NULL,
    record_source VARCHAR(100) NOT NULL
);

-- Satellite: Descriptive attributes
CREATE TABLE sat_customer_details (
    hub_customer_hashkey VARCHAR(32) NOT NULL,
    load_date TIMESTAMP NOT NULL,
    load_end_date TIMESTAMP,
    hash_diff VARCHAR(32) NOT NULL,
    record_source VARCHAR(100) NOT NULL,
    customer_name VARCHAR(200),
    email VARCHAR(200),
    phone VARCHAR(50),
    PRIMARY KEY (hub_customer_hashkey, load_date)
);

Read the full file on GitHub · 474 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago First seen · 474 lines · 24 tokens per session scan B 9168751c20df

Subscribe to this mod's changes

data-engineering-expert is an agent published in the GitHub repository jpoutrin/product-forge (16 stars, last pushed 7mo ago), licensed MIT. It adds 24 tokens to every session and 2,782 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it B with 2 findings (sends data to an external url, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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