Data Integration Analyst, AI & Risk Adjustment

Reveleer

5d•$105,000 - $135,000•Remote

About The Position

The healthcare ecosystem generates an enormous volume of raw data, and most of it is messy, inconsistent, and hard to use. Reveleer's advantage comes from turning that raw material into structured, trustworthy datasets that our platform, our analytics, and our AI can actually leverage. This role sits at the center of that effort. As a Data Integration Analyst on the AI & Data team, you will design and build the systems and data pipelines that move raw healthcare data into structured, reusable form. You will have ownership of ensuring that customer are able to effectively connect their data systems with the Reveleer ecosystem. Your work will span several connected domains: ingesting and transforming customer data for medical record retrieval; building the data foundations for retrospective risk adjustment coding; developing condition-suspecting algorithms that surface likely undocumented diagnoses; and semantically structuring underlying data so it is far more accessible to AI tools. In short, you turn the raw data of the healthcare ecosystem into the structured assets that everything downstream depends on. You will report to the VP of Data Strategy. This is a high-autonomy role with a high ceiling. If you are smart, curious, self-motivated, and driven, you will have significant freedom to decide how the work gets done, and a correspondingly large opportunity for impact, growth, and advancement. We are building a team of exceptional people and strive to create an environment where high-performers can grow and excel.

Requirements

Bachelor's degree in a quantitative, scientific, technical, or related field, or equivalent hands-on experience. Advanced degrees and research backgrounds in quantitative or scientific fields are welcome.
Proven, hands-on coding ability and demonstrable experience working with database technology. We work primarily in SQL Server / T-SQL, but strong experience in any comparable relational database transfers well, the specific platform matters far less than your ability to code and to prove it.
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.

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.
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.