Overview
Analytics engineering sits between raw ingestion and the dashboards and apps that consume data. We design the modeling layer that turns a warehouse full of source tables into a small, well-documented set of marts that the business actually understands — with the conventions, tests, and ownership model needed to keep that layer trustworthy as the team grows.
Reference Architecture
flowchart LR
Raw[("Raw / Source<br/>Schemas")] --> Staging["Staging Models<br/>(1:1 with sources)"]
Staging --> Intermediate["Intermediate<br/>(joins, dedup, cleanup)"]
Intermediate --> Marts["Marts<br/>(Dimensional / Star)"]
Marts --> Semantic["Semantic Layer<br/>(Metrics)"]
Semantic --> BI["BI Tools"]
Semantic --> Apps["Apps / Reverse-ETL"]
Marts -.-> Tests["dbt tests<br/>+ Freshness SLAs"]
Engagement Model
We typically begin with a modeling audit that maps current marts to business processes and identifies the highest-leverage gaps. Build work proceeds one business domain at a time so every release is independently useful to the team.