Data engineering
Pipelines, warehouses, and reporting you can trust.
- Typical scope
- 3–8 sources, warehouse, 10–20 modeled tables
- Timeline
- 2–6 weeks
- Price band
- $799+
The problem you probably have
Two dashboards show different numbers for the same metric, and nobody can say which is right. Decisions are being made on data nobody trusts.
What you get
- A single source of truth with lineage — you can trace any number back to its origin
- Tested transformations, so a schema change upstream fails loudly instead of silently corrupting a report
- Reporting your team can extend without calling us
Typical stack
- Python
- dbt
- Postgres
- BigQuery
- Airflow
Questions on this specifically
Do we need a warehouse, or is Postgres enough?
Below roughly 100GB and without heavy analytical scans, Postgres is usually enough and far cheaper to operate. We will say so rather than selling you infrastructure you do not need.
Two dashboards show different numbers. How do you fix that permanently?
By making every metric a defined, versioned transformation instead of a formula someone rebuilt in each tool. One modeled table becomes the source both dashboards read, and lineage means any number can be traced back to the raw records it came from. Disagreement stops being possible, not just rare.
What happens when a source system changes its schema?
The pipeline fails loudly at the boundary test instead of silently writing garbage into your reports. You get an alert naming the source and the change; the affected models pause rather than propagate. A wrong report you trust is far more expensive than a late one you were warned about.
Which sources can you connect?
Databases, SaaS APIs, webhook streams, spreadsheets, and flat-file exports — in practice, whatever your systems can produce. The awkward sources with no proper API are normal for us and get an extraction approach scoped honestly rather than hand-waved.
Do we need to hire a data engineer to keep this running?
No. The models are documented and tested, and adding a column or a report is designed to be within reach of a technically comfortable analyst. When you outgrow that — real scale, streaming, ML pipelines — you will know, and the foundation will already be right.
Have a project in mind?
Bring us the problem. StratEdge scopes it, staffs it, manages it, and delivers it — one accountable company from start to finish.