Healthcare Data Analyst

About AirPay

AirPay automates dental insurance verification and revenue cycle workflows for dental practices. We process millions of eligibility and benefit transactions annually, sourcing data from payer portals, EDI pipelines, and direct connections. Our data team sits at the intersection of scraper engineering, payer operations, and product, turning raw benefit data into actionable outputs for practices and their patients.

The Role

We’re looking for a Healthcare Data Analyst to own end-to-end analytical investigations across our benefit verification pipeline. This is a research and signal-to-insight role – you’ll take data anomalies, parser inconsistencies, and operational gaps, and turn them into structured findings that drive engineering fixes, product decisions, and QA processes.

You'll report to the Head of Data, working as a peer-level collaborator with engineering, customer success, and operations.

What You’ll Own

• Data quality investigations: Run structured investigations into benefit data anomalies across 100 payers. Triggers are both scheduled (cohort comparisons between data sources: portal-scraped vs EDI vs internal estimates) and signal-driven (customer escalations, operational reports). Output is engineer-actionable findings documents: what’s broken, where, what evidence. Common patterns include coverage percentage inversions, blank-fill in returned data, frequency normalization gaps (e.g., “12 months” vs “1 service year”), and stale data carrying across plan changes.
• Customer issue triage analysis: Own the weekly pattern analysis on customer issue tickets. Categorize ticket types, track volume trends, identify recurring root causes, and surface signal that drives parser fixes, product changes, or QA process updates.
• Ad hoc analytical investigations: When a payer behaves unexpectedly, a customer surfaces a data anomaly, or a hypothesis needs checking, you scope and run the investigation and write up the findings.

What We’re Looking For

Required:

• 3–5 years in healthcare data, payer analytics, insurance operations, or RCM. Dental background not required, but you need to understand how insurance benefit structures work (coinsurance, deductibles, frequency limits, network tiers)
• Strong data skills: comfortable in SQL and Excel/Sheets, can write a structured query, build a pivot, and sanity-check a dataset without hand-holding
• End-to-end research ownership: can start from a vague “something looks off” signal, scope an investigation, run it, and write up clear findings
• Clear written communication. Output is documentation of findings and structured reports, not just raw data

Strong plus:

• EDI X12 familiarity (270/271 eligibility transactions especially)
• Experience at a health tech company, payer, or benefits administrator
• Comfort with Python or scripting for data manipulation
• Experience working with parser output, portal data, or unstructured benefit text

Growth Path

This is the first analyst hire on a growing data team. Several surfaces you'd eventually own – the data quality scorecard, deeper engineering interfaces, the team’s operating rhythm – are still being built. You'd help shape them rather than inherit fully-formed processes. The role anchors day-one as a generalist healthcare data analyst, with specialization paths opening as the team matures: EDI and payer-depth analysis, customer-facing analytics delivery, or clinical/dental content expertise.

Why This Role

• High-signal, low-noise work: you’ll see anomalies across the entire benefit landscape before anyone else does
• Direct impact: your findings drive parser fixes, product features, and QA standards
• Small team with high ownership: no queuing behind a data engineering backlog
• Comp: competitive for mid-level healthcare data roles in NYC