Glossary · Business Analytics
Analytics Engineering
also: dbt · modern data stack · ELT
Definition
Analytics engineering is the discipline of building reliable, tested, version-controlled transformations on top of a cloud warehouse, bridging data engineering and analysis. Tools like dbt, Dagster, and Airbyte formalize a software-engineering workflow for SQL transformations with tests, documentation, and lineage.
The modern data stack (Snowflake/BigQuery plus Fivetran/Airbyte plus dbt plus BI) shifted transformation from ETL scripts to ELT-in-warehouse, and raised the question of who owns the semantic layer. Analytics engineering emerged as the answer: engineers who write SQL transformations with tests, documentation, incremental refresh, and CI/CD. The practice produced dramatic reductions in metric inconsistency, but only when coupled with metric ontology design and strict semantic-layer governance.
Essays on this concept
- Business Analytics
The Analytics Engineering Manifesto: Why dbt Changed the Data Team Operating Model Forever
Before dbt, analysts wrote SQL that nobody reviewed, nobody tested, and nobody documented. The tool was simple — SQL templating with version control. The impact was structural: it created an entirely new discipline.
- Business Analytics
Cohort-Based Unit Economics: Why Monthly Snapshots Lie and How to Build a True P&L by Acquisition Cohort
Your company's monthly revenue is growing 20% year-over-year. Your unit economics are deteriorating. Both statements are true simultaneously — and you'll never see the second one in an aggregate P&L.
- Business Analytics
From Dashboards to Decision Systems: Embedding Prescriptive Analytics Into Operational Workflows
Your company has 47 dashboards. How many of them changed a decision last week? Dashboards describe what happened. Decision systems prescribe what to do next — and the gap between these two is where most analytics ROI evaporates.
- Business Analytics
Metric Ontology Design: Building a Self-Serve Analytics Layer That Doesn't Collapse Under Ambiguity
Ask five people in your company what 'revenue' means and you'll get five different numbers. The problem isn't the data warehouse — it's that nobody agreed on the definitions before building dashboards on top of them.
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