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Data Principles for Analytics Engineering

A comprehensive reference covering best-practice data principles for an Analytics Engineering consultancy. Grounded in real-world practice and authoritative frameworks: DAMA-DMBOK, dbt best practices, the DataOps Manifesto, Google's data principles, and Snowflake well-architected guidance.

This document has been split into four focused pages for readability and searchability:

Sub-pages

  • [[Data-Quality-and-Governance]] — Data quality dimensions (completeness, accuracy, timeliness, consistency, validity, uniqueness), data ownership, stewardship, metadata management, lineage, and cataloguing. Source references appendix.

  • [[Data-Architecture]] — Single source of truth, idempotent ELT, incremental processing, raw/staging/mart separation, and data contracts. Design decisions for structural integrity.

  • [[DataOps-and-Data-Engineering]] — Version control, CI/CD for pipelines, testing data transformations, observability, SLAs/SLOs. The operational backbone. dbt project non-negotiables checklist.

  • [[AE-Consultancy-Delivery]] — dbt model organisation, testing, documentation, macros and packages, metric layer, cloud platform principles, privacy & ethics, consultancy engagement, maturity assessment, phased delivery, and knowledge transfer. Glossary appendix.

Document version: 1.0 | Last updated: April 2026 | Author: AE Consultancy Principles Working Group

  • [[10-Opportunities-for-Additional-Value]]
  • [[Data-Quality-and-Governance]]
  • [[Data-Architecture]]
  • [[DataOps-and-Data-Engineering]]
  • [[AE-Consultancy-Delivery]]
  • [[Hermes Vision]]