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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