Jonny Cromwell
Data Engineer
About
Data engineer with hands-on experience building ELT pipelines, data warehouses, and transformation layers across multiple platforms. Currently focused on healthcare data interoperability, specifically HL7 FHIR R4 data standards. I approach complex problems by combining practical engineering skills with a willingness to learn new domains, whether that means parsing deeply nested clinical JSON structures or standing up containerized database environments from scratch.
Projects
HL7 FHIR End-to-End Data Engineering Pipeline
Status: Pipeline 1 of 2 complete
A production-style ELT pipeline that ingests synthetic HL7 FHIR R4 healthcare data and transforms it into an analytics-ready data warehouse using the medallion architecture.
The problem: FHIR data is deeply nested JSON with extension-based fields, coded terminology systems (LOINC, SNOMED), and cross-resource references. Flattening this into queryable relational tables requires parsing patterns that don’t exist in traditional CSV or flat-file pipelines.
What I built:
- Generated synthetic FHIR R4 patient data (Patient, Encounter, Observation resources) using Synthea in both NDJSON and Bundle formats
- Loaded raw FHIR JSON into PostgreSQL JSONB tables (bronze layer) using Python with batch inserts
- Built dbt models that flatten nested FHIR structures, parse resource references into foreign keys, and extract extension fields like race, ethnicity, and geolocation (silver layer)
- Created dimension and fact tables with calculated ages, lab panel classification, clinical reference ranges, and abnormal result flagging (gold layer)
- Implemented 23 automated data tests validating uniqueness, null checks, and referential integrity across all layers
Tech stack: Python · PostgreSQL 17 · dbt · Docker · Synthea
Planned expansions:
- Pipeline 2: SQL Server + SSIS
Synthetic Health Data Generator
A Python-based synthetic data generator built with the Faker library for creating large-scale healthcare datasets. Designed to produce configurable volumes of patient demographics and daily health records (vitals, blood pressure, weight) partitioned by year and month.
What it demonstrates:
- Generated over 40 million records (10,000 patients across 10 years) for stress-testing ETL pipelines
- Configurable patient count and date range for scalable test data
- Used as source data for building and validating pipelines in both PostgreSQL/Python and SQL Server/SSIS environments
Tech stack: Python · Faker · Pandas
Development Approach
I build these projects collaboratively with AI tools (primarily Claude) to bridge knowledge gaps and accelerate development, while ensuring I understand every component end-to-end. Each project includes documentation I wrote myself explaining the architecture, design decisions, and implementation details. I believe the ability to work effectively with AI tools is itself an engineering skill, which means knowing what to ask, how to validate the output, and when to push back on a recommendation.