Skill Guide
The design, construction, and maintenance of automated pipelines that extract, transform, and load (or extract, load, and transform) data from diverse health sources into analysis-ready formats while ensuring compliance with privacy regulations and data quality standards.
Scenario
You receive a sample dataset of 1000 de-identified patient records with demographics, diagnoses (ICD-10 codes), and lab results in CSV format. Your goal is to create a clean, analysis-ready dataset.
Scenario
Your organization needs a daily updated dataset of all patient encounters from a FHIR API to power a dashboard. The pipeline must be reliable, handle API pagination and potential downtime, and load data into a cloud data warehouse.
Scenario
Your health system is merging with another. You must design a unified data platform that ingests data from two different Epic EHRs, two claims databases, and a new patient-reported outcomes API, supporting both BI and ML workloads.
Used to programmatically author, schedule, and monitor data pipelines. Airflow is the industry standard; Dagster offers stronger data-aware scheduling for complex dependencies.
Cloud-native platforms for storing and querying transformed data. Snowflake and BigQuery are dominant for ELT workloads; Databricks is preferred for unified analytics and ML on a lakehouse architecture.
FHIR is the modern standard for web-based health data exchange. HL7 v2 is a legacy messaging standard still prevalent. OMOP CDM is a standardized data model enabling multi-site observational research and analytics.
dbt enables version-controlled, tested SQL transformations within the warehouse. Great Expectations is used to define and assert data quality expectations (e.g., 'primary key is unique', 'value is between 0 and 120'). SQL is the fundamental language for all transformation logic.
Answer Strategy
Structure your answer around: 1) **Extraction Strategy** (handling pagination, rate limits, delta loads), 2) **De-identification/Transformation** (applying Safe Harbor rules, flattening resources), 3) **Loading & Modeling** (loading into a warehouse, conforming to a model like OMOP), and 4) **Orchestration & Monitoring** (using Airflow, implementing alerts). Sample Answer: 'I'd use an Airflow DAG with tasks to first extract new/updated Patient and Encounter resources using the FHIR `_since` parameter. In a transformation step, I'd apply a de-identification library like ARX to remove or generalize PHI, then flatten the JSON. The cleaned data would be loaded into a Snowflake schema and transformed via dbt into the OMOP CDM. The entire pipeline would have retry logic, logging, and a data quality check on row counts before sending a Slack alert upon completion.'
Answer Strategy
The interviewer is testing problem-solving, ownership, and systemic thinking. Use the STAR method (Situation, Task, Action, Result). Focus on technical diagnosis, root cause analysis, and proactive solution design. Sample Answer: 'Situation: A daily claims report showed a 40% drop in revenue, but source system counts were normal. Task: I needed to diagnose the discrepancy urgently. Action: I immediately checked pipeline logs and found a transformation error where a new CPT code from a payer caused a NULL in a join, silently dropping all records containing it. I fixed the immediate pipeline. For systemic change, I implemented a mandatory data quality test in our dbt model for every column used in a join, asserting no nulls, and set up a 'reconciliation' dashboard comparing source and target counts daily. Result: The pipeline was fixed same-day, and the new tests caught two similar issues in the following month before they impacted reporting.'
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