Dev-Data — Data Engineering & Analysis Guide

Activates by change surface for data pipelines, analytics, SQL-heavy work, schema evolution, backfills, and reporting.

Production-grade data engineering patterns for building reliable data systems.

Before data work, read dev for classification, fast paths, rule authority, family invariants, verification, and safety; current/public evidence follows its Conditional Routes.

Use browser fetch/open/text/get-dom/snapshot only after candidate URLs exist and the claim needs browser-verifiable source evidence.

When to Activate

• Building data pipelines or ETL/ELT processes
• Processing CSV, JSON, Parquet, or Excel files
• Writing analytical SQL, warehouse/lakehouse queries, or transformation models
• Setting up data quality checks or validation
• Performing data analysis, aggregation, or reporting
• Choosing between batch and streaming architectures

Do not activate for plain app CRUD SQL, OLTP query tuning, or transactional schema design. Route those to dev-backend/references/stacks/database.md. This skill owns analytics, ETL/ELT, pipelines, data quality, and reporting.

Modular References

Condition	Reference
Every data task; ingestion, incremental loading, ETL/ELT, dbt, retries, or orchestration	Principles and pipelines
Quality checks, data contracts, schema changes/backfills, review, or delivery	Quality and evolution (DATA-MIGRATION-01, DATA-REVIEW-01)
Analysis/reporting, batch/streaming, storage, or engine selection	Analysis and engines, streaming, tools
ML training, experiments, feature stores, or data versioning	ML pipelines
PII, retention, masking, or governance	Governance, implementation patterns; cxc-dev-security for policy
Analytical SQL/query execution safety, pipeline APIs, observability, or consumers	Queries and companions