Experienced Data Analyst

Gainwell Technologies LLC•Any city, OH

19h•$81,100 - $115,900•Remote

About The Position

The Experienced Data Analyst – Medicaid Quality for Patient Centered Medical Home (PCMH) program role is an individual contributor position responsible for designing, computing, validating, and explaining healthcare quality measures used by state Medicaid agencies, Fee-For-Service (FFS) providers, and managed care organizations. This role serves as a subject‑matter expert for PCMH measures translating complex technical requirements into accurate, audit‑ready measures using large‑scale Medicaid claims, eligibility, pharmacy, and provider datasets. This individual works independently to implement and maintain measure logic across annual specification updates, ensure data quality and methodological integrity, and support internal reviews, client reporting, and audit inquiries. In addition to strong technical execution, this role requires the ability to clearly communicate measure methodology, assumptions, and results to clinical, operational, and leadership stakeholders. The position plays a key role in ensuring Medicaid quality reporting is accurate, consistent, and aligned with regulatory and program objectives.

Requirements

Bachelor’s or Master’s degree in Computer Science, Healthcare Informatics, Biostatistics, MIS, Statistics, or related fields.
10+ years in healthcare reporting, BI, analytics, data warehousing, and medical billing, with SME‑level knowledge of Medicaid/Medicare, payers, providers, or health services.
Strong SQL, Oracle, ETL, DW infrastructure, data extraction/transformation, and experience with reporting tools such as Business Objects.
Proficient in SDLC methodologies and translating business needs into enterprise BI, reporting, and analytical requirements.
Excellent written/verbal communication with strong analytical and problem‑solving abilities for stakeholder engagement.

Responsibilities

Medicaid Quality Measure Expertise: Demonstrated experience interpreting and implementing HEDIS or HEDIS-like specifications within Medicaid and managed care environments, including measurement periods, continuous enrollment, exclusions, and annual specification updates.
Medicaid Program & Data Domain Knowledge: Strong understanding of Medicaid eligibility, enrollment, and managed care models (MCO), along with deep familiarity with claims, encounters, pharmacy, provider, and member data used for quality reporting.
Clinical Coding & Value Set Proficiency: Advanced working knowledge of ICD‑10‑CM, CPT/HCPCS, and NDC codes and the application of standardized value sets in HEDIS measure logic.
Measure Validation, QA & Audit Support: Proven experience validating quality measure results, performing data quality assessments, reconciling outputs, and supporting internal quality reviews, state reporting, and audit inquiries.
Stakeholder & Client Communication: Ability to clearly explain complex quality metric logic, assumptions, limitations, and results to Medicaid clients, clinicians, business stakeholders, and leadership.
Advanced SQL for Medicaid Analytics: Expert‑level SQL skills, including complex joins, performance optimization, applied to large‑scale Medicaid claims and eligibility datasets.
Healthcare Data Modeling: Experience working with normalized and analytical data models for Medicaid claims, encounters, pharmacy, eligibility, provider, and member data to support scalable quality measure computation.
ETL & Data Transformation: Strong ability to design and execute data transformations that convert raw Medicaid source data into analytics‑ready datasets for pay-for-performance and quality reporting.
Python (or Equivalent) for Analysis & Validation: Proficiency using Python or similar tools to support exploratory analysis, measure validation, reconciliation, and automation of quality analytics workflows.
Documented & Version‑Controlled Measure Logic: Experience maintaining reusable, version‑controlled incentive measure logic and documenting methodologies to support annual specification changes and knowledge transfer.