Analytics Engineer
Analytics engineers build the data models and semantic layers that power business intelligence, acting as translators between raw data pipelines and the analysts and executives who consume them. Their English must be precise enough to write data contracts and clear enough to explain model logic to non-technical stakeholders. This path covers the vocabulary, writing, and communication patterns unique to the analytics engineering role.
Topics covered
- dbt & data modelling
- Semantic layers
- Data contracts
- BI stakeholder communication
- Data quality & testing
- Lineage documentation
Vocabulary spotlight
4 terms every Analytics Engineer should know in English:
A formal agreement on the schema, quality rules, and SLAs for a data asset shared between producers and consumers
"We published a data contract for the orders table so downstream teams know what to expect."
The level of detail represented by a single row in a data model
"Before we join these tables, confirm the grain — is it one row per order or per line item?"
A purpose-built data model designed for a specific business domain or team
"The finance mart exposes pre-aggregated revenue figures so analysts don't need to write complex SQL."
The documented chain of transformations from raw source data to a final model
"The lineage graph shows that this revenue figure traces back to three source systems."
📚 Vocabulary Reference
Key terms organised by category for Analytics Engineers:
dbt & Modelling
Data Contracts & Quality
Semantic Layer & BI
Lineage & Governance
Recommended exercises
Real-world scenarios you'll practise
- Writing a data contract for a shared mart and presenting it in a data governance review.
- Explaining dbt model grain and lineage to a business analyst who needs to trust the numbers.
- Communicating a breaking schema change to downstream consumers — proposing a migration timeline and deprecation notice.
- Presenting data quality test failures to stakeholders and recommending remediation steps.