Healthcare Data Engineer: Build Secure, Scalable Pipelines

Rippedboxstation

Manila

On-site

PHP 688,800 - 1,377,600

Part time

14 days+

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Job summary

Rippedboxstation is seeking a part-time Healthcare Data Engineer to design and manage scalable ETL/ELT data pipelines for diverse healthcare data sources. The role requires collaboration with data scientists and analysts to support advanced analytics in a compliant, secure environment.

The position operates on a 20 hours/week schedule aligned to UK Time Zone (9AM–5PM). Remote work from the Philippines is accepted, with cloud-based data processing using AWS or Google Cloud and standard healthcare

Qualifications

Proven experience as a Data Engineer, preferably within healthcare or life sciences.
Strong expertise in designing and managing ETL/ELT processes and data pipelines.
Hands-on experience with at least one major cloud platform: AWS or Google Cloud.
Proficiency in Python, PySpark, and SQL.
Knowledge of healthcare data standards (HL7, FHIR, DICOM) and data interoperability.

Responsibilities

ETL Pipeline Development: Design, implement, and manage scalable and reliable ETL/ELT data pipelines to process diverse healthcare data from various sources.
Data Integration: Extract and consolidate data from disparate sources, including electronic health records (EHRs), real-world datasets, pharmacy sell-out data, and disease-specific surveys.
Data Transformation & Cleansing: Cleanse, validate, and standardize raw healthcare data. Map data to standard medical terminologies (e.g., ICD-10, SNOMED CT, LOINC), remove duplicates, and resolve inconsistencies to ensure high data quality.
Cloud Management: Utilize AWS or Google Cloud services to build, deploy, and monitor data processing solutions and storage infrastructure.
Compliance and Security: Implement and maintain strict data security, privacy, and governance protocols to ensure compliance with regulations such as HIPAA and GDPR, including encryption, access control, and audit trails.
Collaboration: Work closely with data scientists, analysts, and business stakeholders to understand data requirements and ensure the data architecture supports advanced analytics and business intelligence needs.