Risk and Quality Analytics Consultant

Seeking a hands-on Consultant Data Engineer Analytics to support analytics engineering and data enablement for Risk Adjustment and Quality products within Medicare Advantage. This role will focus on building scalable data pipelines, analytics-ready models, validation frameworks, and reporting assets that support suspect identification, chart review, HCC capture, quality gap closure, coder operations, and client value measurement.

The ideal candidate brings strong SQL and Python skills, practical healthcare data experience, and familiarity with Medicare Advantage data domains such as eligibility, claims, encounters, MMR, MOR, MAO, HCCs, provider attribution, chart retrieval, and quality gaps. This role requires a systems thinker who can translate Risk and Quality product requirements into accurate, auditable, and reusable data solutions.

Risk and Quality Analytics & Data Engineering

• Develop and maintain data pipelines supporting Risk Adjustment and Quality workflows, including eligibility, claims, encounters, suspect gaps, chart retrieval, coder review, QA, and submission reporting.
• Ingest, normalize, and reconcile Medicare Advantage datasets, including payer feeds, provider data, clinical data, chart metadata, HCC outputs, quality gap files, and CMS-standard files where applicable.
• Create analytics-ready data models that support capture metrics, dismissal metrics, gap closure, chart throughput, coder performance, quality measures, and client value reporting.
• Analyze Risk and Quality performance trends and create recurring and ad hoc reports for capture, dismissal, gap closure, coder productivity, QA outcomes, provider attribution, and operational KPIs.
• Build validation and reconciliation processes to ensure source data, platform data, and reporting outputs remain consistent and auditable.

Product and Operations Enablement

• Partner with Product, Clinical, QA, and Operations teams to translate Risk and Quality requirements into datasets, metrics, dashboards, and reporting workflows.
• Support operational reporting for chart processing, suspect review, coder decisions, QA outcomes, provider attribution, and member prioritization.
• Develop and maintain analytical reports, dashboards, and summary views that help stakeholders understand performance, data quality, and product impact.
• Enable product enhancements through backend datasets, data contracts, metric definitions, and release validation support.
• Assist with analytics needs for AI-assisted review workflows, coder applications, quality programs, and client delivery reporting.

Platform Optimization & Lean Engineering

• Build reusable SQL and Python components for ingestion, transformation, data quality checks, and reporting automation.
• Optimize data warehouse tables, queries, scheduled jobs, and pipeline execution for scale, performance, and cost efficiency.
• Monitor pipeline health, investigate anomalies, and document root cause and remediation steps for data issues.
• Maintain clear documentation for data lineage, metric definitions, transformations, and operational runbooks.

Cross-Functional Collaboration

• Work closely with Engineering, Analytics, Product, Clinical, QA, and Operations teams to support Risk and Quality implementation and reporting priorities.
• Participate in data mapping sessions, requirements refinement, issue triage, release testing, and client-readiness activities.
• Communicate technical findings and data limitations clearly to both technical and business stakeholders.

Compliance & Data Governance

• Ensure data workflows follow HIPAA, CMS, security, and least-privilege access expectations.
• Implement data validation, auditability, traceability, and documentation standards across Risk and Quality analytics outputs.
• Support controlled handling of PHI, de-identification, retention, and access management requirements.