Data engineering and
analytics
SDEN builds the data pipelines, warehouses, and analytics layers that turn raw product events into metrics teams can defend in a board meeting.
What this domain covers
Data work starts upstream of the warehouse, at the schema. Events are modeled with the same rigor as application data: explicit contracts, versioned schemas, rejected at the door when they don't match.
From there they land in PostgreSQL, BigQuery, or Snowflake by volume, with dbt as the one transform layer and metrics computed against a documented model, not ad-hoc SQL pasted into a chart.
The dashboards we leave behind outlast whoever built them: documented lineage, a freshness guarantee, and defined behavior when upstream data runs late. Anyone can answer 'where does this number come from?' without opening five tools.
Data engineering and analytics: the SDEN defaults
Defaults we ship
- Schema-on-write with explicit data contracts at ingestion
- dbt as the canonical transform layer; SQL is reviewed like code
- Warehouse choice based on volume, not on the loudest vendor
- Dashboards with documented lineage and freshness SLAs
Deliverables
- Event schema definitions checked into the application repo
- dbt project with documented models and tests
- Analytics dashboards (Metabase, Looker, or your existing BI tool)
- Data quality monitoring with alerts on freshness and row-count anomalies
More from
the SDEN blog.
Cornerstone writing from the SDEN team: what AI changes, what it doesn't, and how a senior team ships the difference.
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