Healthcare Data Engineer (AA - 11112025 - PTHDE)
At RippedBoxStation · Manila, Metro Manila, Philippines
- Data and ML
- Part-time
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Where you can work from
Open to the Philippines
“Manila, Metro Manila, Philippines”
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- First seen by us
- 9 October 2026
- Last checked
- 43 hours ago
Job description
From RippedBoxStation’s job post, as they wrote it.
Position: Healthcare Data Engineer
Number of hours: 20 hours/week
Schedule: UK Time Zone - 9AM - 5PM
Key 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.
Read the full description
* 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.
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: Proficiency with services such as AWS Glue, Amazon S3, AWS Lambda, Amazon Redshift, and AWS HealthLake.
* Google Cloud: Proficiency with services like Cloud Healthcare API, BigQuery, Dataflow, and Dataproc.
* Solid understanding of healthcare data standards (e.g. HL7, FHIR, DICOM) and data interoperability challenges.
* Proficiency in programming languages such as Python and PySpark, and experience with SQL.
* Knowledge of data security best practices and experience implementing measures to protect sensitive health information.
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