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Data Engineer | Python | SQL | Data Pipelines | Data Infrastructure | Snowflake | AWS | London, Hybrid
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Data Engineer | Python | SQL | Data Pipelines | Data Infrastructure | Snowflake | AWS | London, Hybrid
Data Engineer | Python | SQL | Data Pipelines | Data Infrastructure | Snowflake | AWS | London, Hybrid
Position Overview
As a Data Engineer, you will play a critical role in building and maintaining the data infrastructure that powers an AI-driven clinical monitoring platform. You will design and operate robust data pipelines that ingest, transform, and distribute sensitive healthcare data originating from Electronic Health Record (EHR) systems, medical devices, and hospital information systems. Reporting directly to senior technical leadership, you will work closely with backend engineers, machine learning engineers, clinical teams, and integration partners to ensure reliable, secure, and compliant flow of patient data, including admissions, conditions, medications, allergies, clinical notes, and vital signs. Your work will directly enable predictive analytics and machine learning models that identify early signs of clinical deterioration, helping clinicians intervene sooner and improve patient outcomes. This is a hands-on role where production-grade data engineering, reliability, and healthcare compliance are essential.
Key Responsibilities
- Design, build, and maintain scalable data pipelines to ingest healthcare data from external systems and databases
- Develop integrations using: REST APIs and webhook-driven workflows
- Database log shipping and change data capture (CDC) mechanisms (e.g., SQL-based systems)
- Transform, validate, and normalize incoming clinical data before loading it into analytical and operational data platforms
- Ensure pipelines are robust, fault-tolerant, and capable of handling large data volumes
- Integrate and manage healthcare data domains including:
- Admissions, discharges, and transfers (ADT)
- Conditions, medications, and allergies
- Clinical and progress notes
- Vital signs and physiological measurements
- Work with healthcare interoperability standards such as HL7 v2 and FHIR
- Partner with external technical teams to support onboarding, troubleshooting, and ongoing data reliability
- Take ownership of operational databases and analytical data warehouses
- Design schemas and transformations to support real-time applications and downstream analytics/ML workloads
- Optimize performance, cost, and scalability of data platforms
- Deploy and operate data pipelines and services in a cloud environment
- Implement monitoring, logging, alerting, and operational dashboards
- Support production reliability, incident response, and continuous improvement
- Ensure all pipelines meet healthcare security and privacy requirements (e.g., HIPAA, SOC 2)
- Apply best practices for handling sensitive health data, including access control, encryption, and audit logging
- Maintain clear documentation of data flows and processes
- Collaborate with data science and ML teams to support:
- Model training and evaluation
- Feature engineering and data labelling workflows
- Work with backend engineers to develop internal tooling for data ingestion and monitoring
- Contribute to architecture discussions to support scaling of data systems
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Required Qualifications
- 5+ years of experience in Data Engineering, Backend Engineering, or a related field
- Strong proficiency in Python
- Strong proficiency in SQL
- Hands-on experience with relational and cloud data platforms, including schema design and performance optimization
- Experience integrating data from SQL-based systems using log shipping or CDC approaches
- Experience building high-throughput data pipelines from ingestion through transformation and storage
- Experience deploying and operating production systems in a major cloud environment
- Familiarity with APIs, webhooks, and event-driven architectures
- Experience working with sensitive or regulated data
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Preferred Qualifications
- Experience integrating with EHR systems
- Familiarity with healthcare data standards such as HL7 and FHIR
- Experience supporting machine learning or data science teams
- Experience with data orchestration or streaming systems
- Background in healthcare, medical devices, or clinical data systems
- Exposure to healthcare compliance and security best practices
What You Bring
- Ownership of data systems end-to-end — from ingestion to analytics
- Strong focus on data quality, reliability, and operational excellence
- Comfort working in complex integration environments with diverse external systems
- Clear communication across engineering, data, and clinical stakeholders
- Motivation to build technology that improves patient care
Why Join
- Work on real-world healthcare problems with measurable impact
- Build data systems that power clinical-grade AI and machine learning
- Take ownership in a fast-growing, mission-driven environment
- Collaborate with a skilled, multidisciplinary team
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