SQL Database Engineer

Anovia (formerly Innovatia Technical Services) is an industry-leading technology outsourcing support provider with expertise in the telecommunications industry. Operating for over 20 years, we specialize in workflow and knowledge processes, as well as technical support, helpdesk and multilingual support services. With over 200 professional experts across the globe, we service some of the world’s most successful Fortune 500 and Fortune 1000 companies.

The SQL Database Engineer II is responsible for extracting, transforming, and validating clinical and administrative data from ambulatory EHR/EMR systems to support CMS MIPS and Value-Based Care reporting. The role requires strong expertise in SQL, HL7, CCDA, FHIR, EDI 837/835, EMR database schemas, and data pipeline engineering to ensure accurate, secure, and audit-ready regulatory submissions.

Responsibilities

• 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).