Senior Clinical Data Operations Analyst III

R1 RCM•Remote, IL, IL

5d•$65,000 - $145,934•Remote

About The Position

The Clinical Data Operations Analyst III is a core technical contributor on the PhareOS Implementation team, responsible for validating and troubleshooting the clinical data that powers PhareOS across HL7, FHIR, and standardized Databricks/dbt pipelines. This role lives at the intersection of raw source data, transformation logic, and downstream product readiness: running SQL-based validation, tracing HL7-to-FHIR transformations end to end, and partnering with engineering, product, clinical, and vendor teams to keep client data accurate, complete, and production-ready. It's hands-on, detective-style work at the center of one of the fastest-growing platforms in healthcare AI, with direct visibility into how clean clinical data translates into measurable outcomes for some of the largest health systems in the country. This position must communicate clearly with both technical and non-technical stakeholders and thrives on solving problems others haven't found yet.

Requirements

Strong SQL skills required — comfortable writing complex validation queries across large, multi-layered healthcare datasets.
Hands-on experience with Databricks SQL and notebooks is essential.
Experience with healthcare clinical and revenue cycle data, including HL7 v2/v3 messaging and FHIR resources/Bundles.
10+ years of experience querying, validating, and analyzing data in SQL, with comfort writing complex, multi-table validation queries.
10+ years of experience working on data pipeline, integration, or implementation projects; healthcare data experience a strong plus.
Experience collaborating cross-functionally with engineering, product, clinical, and/or vendor teams, translating technical findings into clear, actionable evidence.
3+ years of hands-on experience with healthcare clinical data formats and standards, including HL7 v2/v3 messaging, FHIR resources/Bundles, and the transformation logic that connects them.
Strong problem-solving and analytical skills — the instinct to keep digging until you find the actual root cause, not just the symptom.

Nice To Haves

Experience with dbt, Spark SQL, Python, Git, and modern data lake/warehouse platforms (Databricks, Snowflake, or similar) is strongly preferred.

Responsibilities

Serve as a front-line data detective for PhareOS: execute SQL-based validation of HL7, FHIR, and batch healthcare data across every layer of the pipeline, from raw source through standardized tables.
Hunt down and resolve data quality issues — missing records, null propagation, duplicate records, incorrect mappings, and inconsistent relationships — before they ever reach a client or a downstream product.
Trace the full journey of clinical data: review raw source messages, HL7-to-FHIR Bundle transformations, and standardized tables to pinpoint source availability gaps and transformation accuracy issues.
Partner directly with engineering to support and refine dbt models and mapping logic that power PhareOS’s clinical data layer.
Validate HL7 and FHIR message processing across critical clinical fields — event codes, patient class, discharge disposition, payer, diagnosis, procedure, and DRG — ensuring the data driving PhareOS is clinically sound.
Build Databricks SQL queries and notebooks to power validation, field-level research, production triage, and go-live readiness for new and existing clients.
Document validation results, mapping assumptions, known limitations, and recommended remediation paths that both technical and non-technical stakeholders can act on.
Be the connective tissue between engineering, product, clinical, and vendor teams — showing up with clear data evidence and reproducible examples instead of vague symptoms.
Monitor and investigate client-specific data discrepancies in production, and validate fixes to confirm the issue is truly resolved.
Support successful client clinical data implementations from data acquisition through go-live, with particular focus on strategic and/or complex clients and tight timelines.
Communicate technical and healthcare revenue cycle concepts clearly across audiences — from engineers and clinicians to client IT/data contacts — adapting the message without losing the substance.