Solution
We rebuilt the pipeline architecture with explicit contracts, quality checks, and interfaces tailored to clinical and operations users.
Case study
HealthcareHealthcare data foundation
Trusted pipelines and operator tooling that turned fragmented datasets into decisions.
- Duration 5 months
- Team 6 engineers
- Performance Refresh time −70%
Representative product visual — replace with verified project screenshots when available.
Context & challenge
The conditions that made the engagement necessary.
Critical insights were delayed by brittle extracts, unclear ownership, and tools clinicians could not trust.
Approach & solution
How delivery was structured and what the platform needed to support.
Architecture themes
- Medallion
- Data contracts
- Lineage
Outcome & proof
Published outcomes for this case study — not extrapolated beyond the source notes.
- Reliable refresh windows for key datasets
- Shared definitions across teams
- Operator tooling with clear provenance
99.5% pipeline reliability
Refresh time −70%Technology
Stack used for the engagement described here.
- Python
- dbt
- PostgreSQL
- Airflow
- React
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