Data Integration Analyst, AI & Risk Adjustment

Summary

Reveleer delivers a unified platform for risk adjustment, quality improvement, clinical intelligence, and member management in value-based care. The Data Integration Analyst will build data ingestion and transformation pipelines, develop risk adjustment and condition-suspecting data products, and structure healthcare data for analytics and AI applications.

Responsibilities

• Serve as the technical point of contact for receiving customer data (health plans) in whatever format they already use, member, provider, clinic location, chart-retrieval detail, claims, and other source data, rather than requiring them to conform to ours
• Ingest source data as-is into our SQL Server data warehouse, preserving raw data for traceability, and build scalable, repeatable ingestion routines that handle varied and often messy structures and file types
• Write and maintain SQL to transform raw source data into accurate, platform-ready outputs, including load files for the Reveleer medical record retrieval platform
• Monitor pipelines for errors, anomalies, and data quality issues, and remediate them before they affect downstream work
• Build and maintain the data foundations that support retrospective risk adjustment coding workflows
• Develop, test, and refine condition-suspecting algorithms that surface likely undocumented or under-documented diagnoses from the underlying data
• Partner with coding, clinical, and analytics teams to translate domain logic into reliable, production-grade data products
• Design and build semantic data structures that make our underlying healthcare data materially more accessible and useful to AI tools
• As an early priority, take ownership of a significant library of semantic analytics tables currently maintained in our development environment: refactor and clean up the code, migrate it into production, and stand up automated, scheduled refreshes
• Establish durable, repeatable standards for promoting code from development to production and for turning massive, raw healthcare data into well-structured, reusable datasets that downstream analytics and AI can leverage
• Document data models, transformation logic, algorithms, and source-specific rules to build institutional knowledge and reduce rework
• Partner closely with the Data Management, analytics, and platform teams to ensure smooth handoffs and dependable production systems
• Work with the VP of Data Strategy and cross-functional partners to continuously improve how raw healthcare data becomes structured, AI-ready assets

Skills

• • Bachelor's degree in a quantitative, scientific, technical, or related field, or equivalent hands-on experience
• • Proven, hands-on coding ability and demonstrable experience working with database technology
• • Demonstrated ability to design creative, original solutions to parse, structure, and make sense of complex, messy, or unfamiliar data
• • Comfort designing, testing, and refining algorithms or analytical logic against real-world data
• • Experience cleaning, mapping, and validating real-world data from multiple sources and file formats into a required structure
• • Experience building repeatable, maintainable ETL or data-ingestion workflows
• • Strong attention to detail and a structured, methodical approach to data quality and validation
• • Clear written and verbal communication skills, including the ability to work directly with customers and internal teams
• • Self-starter who takes ownership end-to-end and thrives with autonomy
• • Experience in health insurance or another healthcare-related field is ideal but not required, strong candidates without healthcare experience are encouraged to apply
• • Graduate study or research experience in a quantitative or scientific discipline
• • Familiarity with SQL Server Integration Services (SSIS) or comparable ETL tooling
• • Exposure to Python for data manipulation and automation
• Understanding risk adjustment (including retrospective risk adjustment coding, HCCs, or condition suspecting), medical record retrieval, or value-based care operations
• • Exposure to preparing, structuring, or engineering data for AI/ML tools and workflows
• • Experience supporting customer onboarding or client data implementations

Qualifications

Must Haves

• • Bachelor's degree in a quantitative, scientific, technical, or related field, or equivalent hands-on experience
• • Proven, hands-on coding ability and demonstrable experience working with database technology
• • Demonstrated ability to design creative, original solutions to parse, structure, and make sense of complex, messy, or unfamiliar data
• • Comfort designing, testing, and refining algorithms or analytical logic against real-world data
• • Experience cleaning, mapping, and validating real-world data from multiple sources and file formats into a required structure
• • Experience building repeatable, maintainable ETL or data-ingestion workflows
• • Strong attention to detail and a structured, methodical approach to data quality and validation
• • Clear written and verbal communication skills, including the ability to work directly with customers and internal teams
• • Self-starter who takes ownership end-to-end and thrives with autonomy

Nice to Haves

• • Experience in health insurance or another healthcare-related field is ideal but not required, strong candidates without healthcare experience are encouraged to apply
• • Graduate study or research experience in a quantitative or scientific discipline
• • Familiarity with SQL Server Integration Services (SSIS) or comparable ETL tooling
• • Exposure to Python for data manipulation and automation
• Understanding risk adjustment (including retrospective risk adjustment coding, HCCs, or condition suspecting), medical record retrieval, or value-based care operations
• • Exposure to preparing, structuring, or engineering data for AI/ML tools and workflows
• • Experience supporting customer onboarding or client data implementations

Benefits

• Remote opportunity
• Medical, Dental and Vision benefits
• 401k match
• Generous PTO plan