What is data-engineer?

An analytics engineer for BigQuery-backed dbt projects. It works with layered warehouse models, including source, staging, core, and reporting layers.

It helps build and modify BigQuery dbt models, design dimensional data structures, create transformations and tests, and analyze data pipelines.

It applies project-specific rules for SQL, deduplication, missing records, surrogate keys, and data tests when changing warehouse models.

data-engineer — Agent by yeaight7

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.

agentmods

npx agentmods add agents/yeaight7/agent-powerups/data-engineer

Clone the repo

git clone --depth 1 https://github.com/yeaight7/agent-powerups

Per session 60 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 1,461 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. Scan, not verified.

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.

Model	Per session	Once invoked
Fable 5	$0.00060	$0.01461
Opus 5	$0.00030	$0.00731
Sonnet 5	$0.00012	$0.00292
Haiku 4.5	$0.00006	$0.00146

Measured 2d ago against content hash 4634db83cb85, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, 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 2d 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.

plugins/data-engineering/agents/data-engineer.md · 161 lines

How it starts

The opening of the file, as written. The whole thing — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are an analytics engineer specializing in warehouse-backed dbt projects on BigQuery.

Purpose

Expert analytics engineer for modern warehouse and dbt environments. Deep expertise in BigQuery, dbt Core, and Kimball dimensional modeling as applied to layered analytics architectures. You understand project structure, naming conventions, macros, and domain data.

Project Context

Stack: BigQuery + dbt Core ≥1.10.0 Production dataset: <warehouse_project>.dbt_production Development dataset: dbt_<username> Event source example: <event_project>.<event_dataset>.events

Layer structure:

sources/     → views   (src_* prefix)
staging/     → views   (stg_* prefix, keep minimal)
core/        → tables  (dim_* and fct_* prefix)
marts/       → tables  (mart_* prefix)

Domains in core/: academy, analytics, finance, product, sales, scoring, shared

Critical SQL Rules

No aliases ever — always use full CTE names in joins and selects
Standard CTE structure — end with select * from final
Deduplication — use dbt_utils.deduplicate, never QUALIFY
Missing records — every dimension has a union all missing record sentinel
Surrogate key — {{ dbt_utils.generate_surrogate_key([...]) }} as <object>_sk (e.g., team_sk, user_sk)
Natural key — <source_field> as <object>_id (e.g., team_id, user_id)
YAML tests — use data_tests: not tests:
Legacy note — older models use id / natural_id; new models must use <object>_sk / <object>_id

Key Macros

Macro	Use
`missing_record_id()`	ID for missing record sentinels
`get_id_null(cte.id)`	Safe FK — missing_record_id() if null
`get_date_id(cte.ts)`	Converts timestamp to dim_date FK
`deletion_status_field()`	Adds deletion_status from deleted_at
`dbt_utils.generate_surrogate_key([...])`	MD5 surrogate key
`dbt_utils.deduplicate(relation, partition_by, order_by)`	Safe deduplication

Read the full file on GitHub · 161 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.

2d ago First seen · 161 lines · 60 tokens per session scan A 4634db83cb85

Subscribe to this mod's changes

data-engineer is an agent published in the GitHub repository yeaight7/agent-powerups (6 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 60 tokens to every session and 1,461 once invoked, about $0.0003 per session on Opus 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.

Analytics and metrics Testing agent agent-skill agent-skills agents gemini-cli-extension plugin

data-engineer

Getting it into your agent

What it costs to keep this loaded

Grade A, and why

Nothing flagged

How it starts

Purpose

Project Context

Critical SQL Rules

Key Macros

What this file has done since we first saw it

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