The Role

• Build, optimise, and operate reliable ELT pipelines in Python and SQL using modern orchestrators (Prefect, Airflow, or Dagster) to ingest data from heterogeneous international sources, APIs, databases, and lakehouse storage (S3, Apache Iceberg).
• Drive entity resolution and master data management for international hospital entities - mapping raw source data to canonical structures and maintaining standardised vocabularies.
• Establish strict data contracts (Pydantic, dbt contracts) and schema management - ensuring dataset reproducibility, lineage tracking, and automated validation across all platform pipelines.
• Optimise data storage, query execution, and compute costs across AWS and Snowflake - keeping data assets secure and cost-effective at scale.
• Implement automated testing and deployment workflows for data pipelines via GitHub Actions and IaC (Terraform).
• Partner directly with Analytics Engineers, Data Scientists, and domain methodology experts to deliver documented, research-grade, and production-ready datasets.

Your Profile

• Advanced Python and analytical SQL for complex data ingestion across REST APIs, databases, and cloud lakes (S3/Iceberg).
• Hands-on Prefect, Airflow, or Dagster in production.
• Practical entity resolution and record linkage: Splink, dedupe, or recordlinkage.
• Schema management and data contracts: Pydantic, dbt contracts, or JSON Schema.
• AWS production environment depth (S3, ECS/EC2) and Snowflake.
• Automated CI/CD for data pipelines via GitHub Actions.
• 5+ years in data engineering with ownership of a core platform through build, launch, and iteration.
• Ontology and semantic modelling experience.
• Healthcare domain context a plus.
• Bachelor's or Master's in Computer Science, Data Science, Software Engineering, or a related quantitative field.
• Highly structured, detail-oriented, and comfortable working at the intersection of data engineering and domain complexity.