SQL Database Engineer (5+)
anovia | 11 days ago | Bengaluru

  • Extract, transform, and validate patient clinical data from EMR/EHR systems using HL7 v2.x, CCDA, and FHIR bundles.
  • Interpret HL7 segments (PID, PV1, ORC, OBR, OBX, DG1, RXA, etc.) for clinical measure mapping.
  • Troubleshoot inconsistent or malformed HL7 messages and resolve data quality gaps with vendors.
  • Build robust parsing logic to normalize data across heterogeneous EMR systems.

EMR/EHR Database Querying & Measure Data Retrieval

  • Query EMR databases (NextGen, eCW, Greenway, Athena, etc.) to extract structured and unstructured clinical data required for MIPS measures.
  • Understand vendor-specific table schemas, metadata dictionaries, and proprietary data models.
  • Translate measure specifications into SQL logic based on CMS MIPS calculation rules.
  • Validate measure completeness, timeliness, and accuracy prior to client submission.

Technical Standards, Architecture Alignment & Governance

  • Maintain and enforce engineering implementation standards, documentation requirements, and build methodologies.
  • Ensure all engineering work aligns with defined solution architecture, technology standards, and security baselines.
  • Lead efforts to improve standard configurations, automation, templates, and reusable frameworks.
  • Ensure engineering quality and consistency through audits, peer reviews, and structured validation.

Data Pipeline Engineering & Transformation

  • Build scalable ingestion pipelines using SQL, Spark (batch & streaming), Python, Databricks, EMR, or OCI Data Flow.
  • Develop metadata-driven pipelines supporting multiple clients and EMR platforms.
  • Implement data quality frameworks (DQ checks, thresholds, reconciliation reports).
  • Maintain robust lineage, audit trails, and governance compliance for CMS readiness.

Regulatory Data Support for MIPS & Value-Based Care

  • Support MIPS Advisors with extraction of numerator/denominator data for clinical quality measures.
  • Produce audit-ready documentation and reproducible calculations for CMS submission.
  • Map EHR data elements to CMS measure logic, identifying gaps or vendor remediation needs.
  • Validate that clients meet annual CMS submission timelines and data completeness requirements.

Cross-Functional Collaboration

  •  
  • Translate technical data issues into actionable insights for non-technical MIPS Advisors.
    • Coordinate with EMR vendors to resolve data extraction failures or schema changes.
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