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PT DAYA CIPTA MANDIRI SOLUSI Linkedin · Posted 29d ago

Data Operations Specialist (Data Ops) – Integrated HIS, EMR, ERP

Bandung

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Indexed description

Requirements

Qualifications

Education

  • Bachelor’s degree in Information Systems, Data Management, Health Informatics, Computer Science, or related field.
  • Certification in Healthcare Data Analytics, SQL, or Cloud Data Platforms (e.g., AWS, Azure) is a plus.

Experience

  • 2–5 years in data operations, data quality, or ETL support, preferably in healthcare.
  • Direct experience working with HIS, EMR, and ERP systems (e.g., Epic, Cerner, Meditech, SAP, Oracle, Microsoft Dynamics).
  • Experience supporting data pipelines for analytics or machine learning (not just reporting).
  • Familiarity with labelling tools (e.g., Labelbox, Doccano) or data curation workflows is a plus.

Technical Skills

  • Intermediate SQL (joins, window functions, data type casting, performance tuning).
  • Basic Python or R – enough to validate data (pandas), call APIs, or rerun simple ETL scripts.
  • Understanding of data formats (JSON, Parquet, Avro, HL7/FHIR).
  • F Responsibilities

Job Summary

The Data Operations Specialist ensures high-quality, reliable, and continuous data flow between the Integrated Hospital Information System (HIS), Electronic Medical Record (EMR), and Enterprise Resource Planning (ERP) modules. Unlike a traditional data entry role, this position actively supports AI Engineers, Data Scientists, and MLOps by preparing, validating, monitoring, and troubleshooting production data pipelines that feed predictive models, clinical decision support, operational analytics, and automated workflows. The role sits at the intersection of healthcare operations and machine learning infrastructure.

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Key Responsibilities

  • Data Pipeline Operations for AI/ML
  • Operate and monitor automated data extraction pipelines from HIS, EMR, and ERP into data lakes/warehouses used by AI models.
  • Ensure real-time/near-real-time data feeds for AI applications (e.g., patient deterioration prediction, length-of-st
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