Design a scalable data pipeline to ingest, transform, and store multi‑source clinical trial data (EHR, lab, imaging) into a central data warehouse, ensuring data quality, lineage, and regulatory compliance. Outline the key components, data flow, and monitoring strategy.
onsite · 3-5 minutes
How to structure your answer
CIRCLES framework + step‑by‑step strategy (120‑150 words, no story)
Sample answer
Using the CIRCLES framework, I first clarified the context: a multi‑site Phase III trial requiring ingestion of EHR (FHIR), lab (HL7), and imaging data into a CDISC‑compliant warehouse. I identified key components: an ingestion layer with Kafka for real‑time streams, a transformation layer using Spark for ETL and CDASH mapping, and a storage layer comprising a data lake for raw files and a relational warehouse for SDTM tables. I recommended data quality rules (range checks, consistency checks) and automated lineage tracking via metadata cataloging. I clarified regulatory constraints (21 CFR Part 11, GDPR) and built audit trails. I evaluated scalability by modeling peak data volume (10 TB/day) and implemented auto‑scaling clusters. Finally, I summarized the monitoring strategy: Prometheus for metrics, Grafana dashboards, and alerting on data quality drift. This design ensures compliance, traceability, and performance.
Key points to mention
- • Data lineage and audit trail for regulatory compliance
- • Adherence to clinical data standards (CDISC, CDASH, SDTM, FHIR)
- • Scalability and fault tolerance (auto‑scaling, partitioning, retry logic)
Common mistakes to avoid
- ✗ Ignoring data lineage and audit requirements
- ✗ Underestimating data volume and peak load
- ✗ Overlooking clinical data standards and mapping