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Data & AI
Data engineering & analytics
Pipelines and a warehouse that make your numbers consistent enough that two teams stop arriving with different answers.
- Project or embedded team
- From USD 28,000
- Updated
The short answer
Data engineering builds the pipelines and warehouse that consolidate business data into one trustworthy source. LDelight implements ingestion, transformation and modelling with tested, version-controlled logic so the same question always returns the same answer.
Key takeaways
- One warehouse, one definition per metric
- Transformation logic version-controlled and tested like code
- Data quality checks that alert before a dashboard misleads someone
- Works with your existing BI tool rather than replacing it
The classic symptom is two teams presenting different revenue figures, both defensible, because each defined the metric in its own spreadsheet. The fix is not a better dashboard; it is a shared, tested definition upstream of every dashboard.
Ingest, model, serve
Source systems land raw in the warehouse. Transformations run in dbt with tests and documentation, version-controlled and code-reviewed. The serving layer exposes clearly-named models your BI tool consumes, with metric definitions living in one place.
Quality as a first-class concern
Freshness, volume, uniqueness and referential checks run with every pipeline. A failing check alerts the owning team before anyone builds a decision on the number.
What you get out of it
-
One number per metric, agreed and documented
-
Reports refreshed automatically
-
Data issues caught before they reach a decision
-
Analysts freed from assembling spreadsheets
Talk to an engineer about Data engineering & analytics
A 30-minute scoping call. No slide deck, no obligation — you leave with a written recommendation.
What's included
- Source system audit and data inventory
- Warehouse design (Snowflake, BigQuery or Redshift)
- Ingestion pipelines
- dbt transformation layer with tests and documentation
- Metric definitions and semantic layer
- Data quality monitoring and alerting
- BI dashboards for the core questions
- Team training on the model
Technology we use
- Snowflake
- BigQuery
- Redshift
- dbt
- Airflow
- Fivetran
- Airbyte
- Looker
- Power BI
- Metabase
- Python
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