Description
Machinify is a leading healthcare intelligence company with expertise across the payment continuum, delivering unmatched value, transparency, and efficiency to health plan clients across the country. Deployed by over 85 health plans, including many of the top 20, and representing more than 270 million lives, Machinify brings together a fully configurable and content-rich, AI-powered platform along with best-in-class expertise. We’re constantly reimagining what’s possible in our industry, creating disruptively simple, powerfully clear ways to maximize financial outcomes and drive down healthcare costs.
We're seeking a Senior Data Analyst to join our Data Engineering team who brings three things together: deep healthcare domain expertise that lets you engage health plan clients directly on payment and process questions; the ability to quickly learn and work across Machinify's internal products — from data ingestion through claims adjudication; and a drive to develop and build AI-driven workflows that continuously raise the bar on what our analyst function can do. This is not a pure back-office role — you'll be the bridge between client needs, internal product knowledge, and data-driven outcomes.
What You’ll Do
- Collaborate with clients and internal stakeholders to understand payment integrity requirements, document process flows, and integrate analytical outputs with client workflows and downstream adjudication systems
- Develop deep understanding of Machinify's internal processes and how they integrate together — from data ingestion through claims adjudication — ensuring analytical solutions are accurate, traceable, and aligned with business needs
- Actively drive AI adoption: prompt and iterate with LLMs to accelerate analysis, identify processes where AI can replace manual workflows, and contribute to AI-assisted pipeline development
- Write and maintain complex analytical SQL against large-scale claims datasets: window functions, CTEs, multi-table joins, query optimization, and production-quality logic that runs reliably at scale
- Identify and build AI-driven automation workflows that replace or accelerate manual analytical processes — moving from one-off prompts to repeatable, team-shareable tooling
- Design and implement data transformations that convert raw medical and institutional claims into canonical models, applying healthcare coding standards (ICD-10, CPT, HCPCS, UB-04, CMS-1500) throughout
- Drive payment integrity analysis — identify overpayment patterns, coordination of benefits issues, and billing edit opportunities by applying deep knowledge of payer policy, CMS regulations, and provider reimbursement methodologies
- Conduct data quality audits and build monitoring frameworks that surface anomalies before they reach production
- Support onboarding of new client data feeds, ensuring smooth integration with minimal disruption
- Partner with engineers, clinical staff, and product managers to translate complex healthcare requirements into data solutions
- Mentor analysts and engineers on healthcare domain questions — serving as the go-to SME on coding, claims adjudication, and payer policy
- Document transformation logic, data lineage, and analytical methodologies to support knowledge transfer and audit readiness
What You Bring
- 5+ years of data analysis experience and working directly with healthcare claims data
- Deep knowledge of medical claim types (professional and institutional), coding systems (ICD-10-CM/PCS, CPT, HCPCS), and standard form formats (UB-04, CMS-1500)
- Demonstrated experience using LLMs or AI tools to accelerate analytical work — not as a novelty, but as a repeatable part of your workflow
- Expert-level SQL: complex analytical queries, window functions, CTEs, performance tuning against datasets of hundreds of millions of rows
- Hands-on payment integrity experience: overpayment detection, COB, billing edits, or claim editing logic
- Familiarity with CMS regulations and commercial payer guidelines as they apply to claims adjudication and payment policy
- Strong communicator — able to discuss healthcare process and payment logic directly with health plan clients and non-technical stakeholders
- Ability to translate ambiguous business and clinical requirements into precise, well-documented data logic
- Working knowledge of provider reimbursement methodologies — MS-DRG/APR-DRG pricing, fee schedules, per diem, and capitation
- Proficiency with Spark SQL or a comparable distributed query engine — writing and optimizing queries against large-scale data environments is a daily expectation
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