What is senior-data-analytics-engineer?

A set of guidelines for building analytics data pipelines and trustworthy metrics. It covers moving and transforming data, warehouse models, SQL performance, and data quality.

Use it to design ELT pipelines, star or snowflake schemas, incremental loads, dimensional models, analytical SQL, and tests for data and metric correctness.

It helps prevent incorrect reports caused by unclear table grain, mishandled history, non-repeatable loads, late data, and metrics that do not calculate what users expect.

How Jm-Paunlagui/CATHERINE configures Claude Code

Reuse

Borrowing it

Nothing to install: this file belongs to Jm-Paunlagui/CATHERINE. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/Jm-Paunlagui/CATHERINE/main/.claude/skills/senior-data-analytics-engineer/SKILL.md

Clone the repo

git clone --depth 1 https://github.com/Jm-Paunlagui/CATHERINE

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/jm-paunlagui/catherine/senior-data-analytics-engineer/github.svg)](https://agentmods.dev/skills/jm-paunlagui/catherine/senior-data-analytics-engineer)

Your own site

<a href="https://agentmods.dev/skills/jm-paunlagui/catherine/senior-data-analytics-engineer"><img src="https://agentmods.dev/badge/skills/jm-paunlagui/catherine/senior-data-analytics-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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/jm-paunlagui/catherine/senior-data-analytics-engineer"><img src="https://agentmods.dev/badge/skills/jm-paunlagui/catherine/senior-data-analytics-engineer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 131 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,383 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.

Model	Per session	Once invoked
Fable 5.1	$0.00131	$0.01383
Opus 5.5	$0.00052	$0.00553
Sonnet 5.5	$0.00026	$0.00277
Haiku 4.5	$0.00013	$0.00138

Measured 2mo ago against content hash 29b5e0d80d06, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

senior-data-analytics-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 2mo 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.

.claude/skills/senior-data-analytics-engineer/SKILL.md · 59 lines

How it starts

The opening of the file, as written. The whole thing — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Senior Data Analytics Engineer

You are a Senior Data Analytics Engineer. Correct, reproducible, performant analytics — from raw source to trusted metric.

Dimensional modeling

Star schema first: narrow, conformed dimensions + numeric fact tables at a declared grain. State the grain of every fact table explicitly (one row per _____).
Facts: additive (sum anywhere), semi-additive (sum across some dims, e.g. balances not across time), non-additive (ratios — store numerator/denominator, compute the ratio at query time, never average an average).
Slowly Changing Dimensions: Type 1 (overwrite), Type 2 (new row + effective-from/to + current flag — preserves history), Type 3 (prior-value column). Pick per business need for history.
Surrogate keys for dimensions; keep natural/business keys as attributes. Conformed dimensions shared across facts.

ELT / pipeline design

Prefer ELT (load raw, transform in-warehouse) with layered models: raw/staging → cleaned/intermediate → marts. Each layer testable and rebuildable.
Idempotent, incremental loads: merge/upsert on keys; watermark on an updated-at column; late-arriving data handled explicitly. Full-refresh must reproduce the same result.
Data contracts at ingestion boundaries; fail loudly on schema drift.

SQL for analytics

Window functions (SUM() OVER, LAG/LEAD, ROW_NUMBER, RANK) for running totals, period-over-period, deduplication, and cohorts — not self-joins.
CTEs for readable multi-step logic; be aware of materialization/optimizer behaviour on the engine.
Performance: partition/cluster large fact tables on the common filter (usually date). EXPLAIN PLAN; index or partition-prune selective predicates. Push filters before joins, joins before aggregation, aggregation before sort. Materialized views for expensive, frequently-read rollups; refresh strategy stated.
On Oracle via the oracle-mongo-wrapper: bind variables always (never interpolate), buildWindowExpr for windows, withCTE/withRecursiveCTE, $group/$sum/$avg + $having, and createPerformance().explainPlan().

Read the full file on GitHub · 59 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.

2mo ago First seen · 59 lines · 131 tokens per session scan A 29b5e0d80d06

Subscribe to this mod's changes

senior-data-analytics-engineer is a skill published in the GitHub repository Jm-Paunlagui/CATHERINE (2 stars, last pushed 2d ago), licensed Apache-2.0. It adds 131 tokens to every session and 1,383 once invoked, about $0.0005 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 2026-09-05.

Analytics and metrics Databases

senior-data-analytics-engineer next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
senior-data-analytics-engineerJm-Paunlagui/CATHERINE	Skill	2	2d ago	Apache-2.0	A	131	Claude Code
create-prprisma/orm	Skill	48k	yesterday	Apache-2.0	A	48	Claude Code, Codex
schema-explorationlangchain-ai/deepagents	Skill	31k	yesterday	MIT	A	57	Claude Code, Codex
atlas-mssqlariga/atlas	Skill	8.8k	yesterday	Apache-2.0	A	163	Claude Code
ha-data-storesshiwenwen/hope-agent	Skill	1.9k	yesterday	MIT	A	115	Claude Code, Codex
nornicdb-cypher-queriesorneryd/NornicDB	Skill	923	yesterday	MIT	A	79	Claude Code, Codex
volcengine-rds-postgresqlbytedance/agentkit-samples	Skill	484	today	Apache-2.0	A	63	Claude Code, Codex

CATHERINE: Skill for Claude Code