Remote Risk Data Analyst Jobs
Remote Risk Data Analyst jobs are open across finance, insurance, fintech, and healthcare at companies building distributed risk and analytics teams, from entry-level analysts supporting credit or operational risk functions to senior analysts leading model validation and reporting. Employers hiring remotely right now include Block, M&T Bank, and Turner & Townsend Pty Limited. Find a role that fits below and apply directly.
Find Remote Risk Data Analyst JobsOverview
Showing 5 of 19+ Remote Risk Data Analyst jobs











Data Integration Analyst, AI & Risk Adjustment
Remote Opportunity
About Reveleer
Reveleer delivers a unified platform spanning risk adjustment, quality improvement, clinical intelligence, and member management for health plans and provider organizations navigating the complexity of value-based care. Trusted by 80+ customer organizations nationwide, the platform integrates data, analytics, and intelligent workflow automation into one governed system designed to support traceable documentation across diagnoses, quality measures, and submissions. With regulatory expertise and transparent, human-in-the-loop AI at its core, Reveleer supports organizations working to advance care quality, strengthen documentation integrity, and sustain the operational readiness needed to navigate audits with confidence.
Why This Role Is Important
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.
What You'll Do
Data Ingestion & Integration
- 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.
Retrospective Risk Adjustment & Condition Suspecting
- 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.
Semantic Structuring for AI
- 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.
Quality, Documentation & Collaboration
- 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.
Who Thrives in Role
We are looking for exceptionally smart, highly motivated people with a very high ceiling, the kind who are energized by ambiguity, take ownership without being asked, and consistently find a better way. If that is you, this role offers unusual freedom to operate and real room to grow.
- You bring rigorous analytical and problem-solving skills, ideally developed in a quantitative or scientific discipline. We especially welcome candidates transitioning from academic or research careers, for example, graduate study or research experience in fields such as molecular biology, genetics, physics, chemistry, or other quantitative sciences, who want to apply their talents in a fast-moving, high-impact commercial environment.
- You have demonstrated aptitude in designing data transformation systems that generate meaningful real-world outcomes. You have hands-on, demonstrable experience with database technology; the specific platform matters far less to us than your ability to learn quickly, build, and show real working competence.
- You are driven by autonomy and results. Given freedom, you run with it, and you want to be somewhere that recognizes and rewards that.
Qualifications
Required
- 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.
Preferred
- 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.
WHAT YOU'LL RECEIVE:
- Competitive salary
- Medical, Dental and Vision benefits
- 401k match
- Generous PTO plan
Our compensation reflects the cost of labor across several US geographic markets. Pay is based on several factors including market location and may vary depending on job-related knowledge, skills, and experience.
Reveleer E-Verifies all new hires.
Reveleer is an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, veteran status, disability status or genetic information, in compliance with applicable federal, state and local law.
See All 19 Remote Risk Data Analyst Jobs
Find roles that match your experience and apply in just a few clicks.
Find Remote Risk Data Analyst JobsRemote Risk Data Analyst Job Market
Who's Hiring



Top Industries Hiring
- Human Resources
- Banking & Financial Services
What Employers Look For
The qualifications that appear most often in remote risk data analyst jobs.
- Bachelor's degree in statistics, mathematics, finance, economics, or a related quantitative field
- Proficiency in SQL for querying large financial or operational datasets
- Experience with Python or R for statistical modeling and data analysis
- Familiarity with risk frameworks such as credit risk, market risk, or operational risk
- Knowledge of regulatory requirements including Basel III, CCAR, CECL, or DFAST
- Experience with data visualization tools such as Tableau, Power BI, or similar platforms
Tips for Your Remote Risk Data Analyst Job Search
Show async communication skills upfront
Remote risk teams depend on written handoffs and documented analysis. Include a brief sample of a risk summary, model write-up, or data memo you've produced independently. It signals you can work without real-time supervision, which remote hiring managers screen for early.
Apply early to remote roles that fit
Migrate Mate lists remote risk data analyst openings from across the U.S. in one place, so you can find roles that match your background and apply directly. Remote postings attract high volume quickly, so applying within the first few days of a listing going live improves your odds.
Highlight your remote risk toolset explicitly
Name the tools you actually use: SQL, Python, R, Tableau, or risk-specific platforms like SAS Risk or Moody's Analytics. Remote employers can't observe your workflow in person, so your application needs to make your technical environment visible and credible.
Prepare for async-style remote interviews
Many remote-first risk teams use recorded video questions or written case submissions before any live interview. Practice explaining a risk model decision or an analysis tradeoff in clear, structured writing. Remote interviewers are assessing how you communicate without body language cues.
Remote Risk Data Analyst Jobs: Frequently Asked Questions
How do I get a remote risk data analyst job?
Target companies with distributed data or risk teams, including fintech platforms, digital insurers, and financial services firms that operate without a centralized office. Remote employers screen hard for self-direction, clear async written communication, and proficiency in SQL, Python, and risk modeling tools. A portfolio showing independent analysis work, clean documentation, and stakeholder-ready outputs gives you a clear edge over candidates with similar credentials.
Which companies hire remote risk data analysts?
Employers currently hiring remote risk data analysts include Block, M&T Bank, and Turner & Townsend Pty Limited, per current remote listings on Migrate Mate as of September 2026. Remote-first financial services firms, insurtech and fintech companies, and large banks with distributed analytics teams account for the bulk of these openings.
Can you get a remote risk data analyst job with no experience?
Yes, but remote entry-level risk data analyst roles are harder to land because employers expect you to work independently from day one without in-person support. Companies most likely to hire remote entry-level candidates include fintech startups and digital lenders. Showing a portfolio with risk-related projects, strong SQL and Python skills, and evidence of self-directed work can open the door when experience is thin.
Do you need a degree for remote risk data analyst jobs?
Not always. Many remote employers prioritize demonstrated skills over formal credentials, especially in fintech and data-heavy startups. What matters most is your ability to build and interpret risk models, communicate findings clearly in writing, and work without supervision. Certifications in data analysis or risk management, combined with a strong project portfolio, can substitute for a traditional degree in a meaningful share of remote postings.
Which industries hire the most remote risk data analysts?
The sectors hiring the most remote risk data analysts are Human Resources and Banking & Financial Services, based on current remote listings on Migrate Mate as of September 2026. These industries rely on distributed analytics teams to monitor credit, operational, and compliance risk across geographies without requiring analysts to be on-site.
See All 19 Remote Risk Data Analyst Jobs
Find roles that match your experience and apply in just a few clicks.
Find Remote Risk Data Analyst Jobs