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 at SDEN starts upstream of the warehouse — at the schema. We model events with the same care as application data: explicit contracts, versioned schemas, and rejection at the boundary when the data does not match. The pipeline then lands events into a warehouse (PostgreSQL, BigQuery, or Snowflake depending on volume), where dbt becomes the canonical transform layer and the metrics are computed against a documented model, not against ad-hoc SQL pasted into a dashboard.
Analytics deliverables are dashboards that survive the engineer who built them. Each chart has a documented data lineage, a freshness guarantee, and a defined behavior when the upstream data is late or missing. The team 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
What we refuse to ship
We will not ship a dashboard that nobody can explain. Metrics that cannot be traced back to a source event get rejected, not approximated.
Data engineering & analytics
questions we get asked.
Direct answers to the questions we get asked the most. If yours isn't covered, write to the team.
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.
Data engineering meets AI: why trustworthy pipelines are the precondition
Every AI feature that holds up in production sits on top of a data layer you can defend. What it takes to build that layer — and how AI is reshaping the work itself.
AI audit for founders: what to assess before you invest more
An AI audit inventories every integration a business already runs, ranks the risk, and gives a defensible build-or-buy verdict — before the next investment.
How AI is rewriting business operations — and where it still has to earn trust
AI is moving from demo to production inside operating businesses. What changes — and what to refuse — when intelligence becomes a load-bearing part of the stack.
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