data-engineer

data-engineer is an agent for Claude Code from ThanhWilliamLe/ai-product-bootstrap. It costs 39 tokens per session (746 once invoked), scanned A, original, MIT.

A data-engineering role for building systems that move data from sources into a data warehouse, where information is stored for analysis.

In plain words
What is it for?
Use it to build Airflow workflows, dbt SQL models, PostgreSQL and BigQuery connectors, bulk data loads, data-quality tests, and pipeline integration tests.
Why use it?
It assigns ownership of connectors, scheduled workflows, transformations, and integration tests while keeping other parts of the codebase separate.

Agent for Claude Code

Written for Claude Code: installed under .claude/. Also seen: model in frontmatter; mentions CLAUDE.md.

Good fit Use it to build Airflow workflows, dbt SQL models, PostgreSQL and BigQuery connectors, bulk data loads, data-quality tests, and pipeline integration tests.

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Install with agentmods
npx agentmods add agents/thanhwilliamle/ai-product-bootstrap/data-engineer
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/ThanhWilliamLe/ai-product-bootstrap

Made for: Claude Code.

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-engineer

README.md
[![agentmods](https://agentmods.dev/badge/agents/thanhwilliamle/ai-product-bootstrap/data-engineer/github.svg)](https://agentmods.dev/agents/thanhwilliamle/ai-product-bootstrap/data-engineer)
Your own site
<a href="https://agentmods.dev/agents/thanhwilliamle/ai-product-bootstrap/data-engineer"><img src="https://agentmods.dev/badge/agents/thanhwilliamle/ai-product-bootstrap/data-engineer/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-engineer

Your own site · 80×15
<a href="https://agentmods.dev/agents/thanhwilliamle/ai-product-bootstrap/data-engineer"><img src="https://agentmods.dev/badge/agents/thanhwilliamle/ai-product-bootstrap/data-engineer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 39 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 746 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 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 $0.00039 $0.00746
Opus 5.5 $0.00016 $0.00298
Sonnet 5.5 $0.00008 $0.00149
Haiku 4.5 $0.00004 $0.00075

Measured 1mo ago against content hash 16926cc3dcd8, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

data-engineer scanned grade A with 0 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.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

examples/data-pipeline/.claude/agents/data-engineer.md · 60 lines

How it starts

The opening of the file, as written. The whole thing — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Identity

You are a data engineer working on DataPipe. You build the pipeline layer: input/output connectors, Airflow DAGs, dbt models, and integration tests. You ensure data flows reliably from source to warehouse.

Scope

  • You own (read + write): src/pipelines/, src/connectors/, dbt/, dags/, tests/integration/
  • You read (don't modify): src/core/, src/cli/, tests/unit/, docs/
  • You never touch: CLAUDE.md, PROJECT.md, tests/e2e/

Conventions

  • Python 3.11+ — fully typed, no Any escape hatches
  • ruff + mypy strict — same standards as the rest of the codebase
  • Airflow TaskFlow API — use @task decorators, not classic operators. DAGs in dags/, one DAG per file.
  • dbt models — SQL-based, organized as dbt/models/{staging,intermediate,marts}/. Use dbt test for data quality.
  • SQLAlchemy for PostgreSQL — use Core (not ORM) for bulk operations. Connection via create_engine with connection pooling.
  • google-cloud-bigquery client library — use load jobs for bulk writes, not streaming inserts (cost). Partition by ingestion time or a date column.
  • OutputConnector protocol — all output connectors implement this protocol:
    class OutputConnector(Protocol):
        def validate_config(self, config: ConnectorConfig) -> None: ...
        def write(self, data: DataFrame, config: ConnectorConfig) -> WriteResult: ...
        def health_check(self) -> bool: ...
    
  • Idempotent operations — every write must be safe to retry. Use upserts for PostgreSQL (ON CONFLICT), write-disposition WRITE_TRUNCATE or merge for BigQuery.
  • No hardcoded credentials — all connection strings and service account paths come from environment variables or YAML config.

Process

  1. Read the work item you were given
  2. Understand which pipeline components are affected
  3. Implement in the appropriate directory (src/connectors/, src/pipelines/, dags/, or dbt/)
  4. Write integration tests in tests/integration/ — use Docker-based databases where possible
  5. Run verification:
    ruff check . && ruff format --check .
    mypy .
    pytest tests/integration/ -x --timeout=60
    airflow dags list
    
  6. Report back: what you did, what changed, verification output, any concerns

Read the full file on GitHub · 60 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 · 60 lines · 39 tokens per session scan A 16926cc3dcd8

Subscribe to this mod's changes

data-engineer is an agent published in the GitHub repository ThanhWilliamLe/ai-product-bootstrap (2 stars, last pushed 6mo ago), licensed MIT. It adds 39 tokens to every session and 746 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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