Senior Product Data Analyst

Clearwave Corporation•Atlanta, GA

About The Position

The Senior Product Data Analyst will be the analytical backbone for active product initiatives around Eligibility AI, Revenue Analysis and Client Outcomes. You will also support the broader Product team's move toward outcome-based measurement across Scheduling, Registration, Intake and Payments. This role sits at the intersection of healthcare operations and data. Strong SQL and BI skills are essential, but so is a working understanding of how patients actually move through scheduling, eligibility, intake and payments — and how that journey drives revenue, staff time and patient satisfaction for our clients. We are looking for someone who can speak both languages: query the data and explain what it means for a practice's front desk, billing team and bottom line. Clearwave's Product Management team operates under a Product Operating Model, which means every product manager is expected to lead with continuous discovery, define success metrics before building and report on business outcomes rather than just features shipped. This Data Analyst role supports turning scattered exports, portal reports and Salesforce data into the metrics, dashboards and analysis our business needs to prove value and make evidence-based prioritization calls.

Requirements

3+ years of experience in healthcare, health-tech or another patient-facing industry, with hands-on exposure to patient engagement, eligibility, scheduling, registration or revenue cycle workflows.
Practical understanding of how patients move through the scheduling, eligibility, intake and payment journey, and how that journey affects collections, denials and patient satisfaction.
Business acumen: the ability to connect a data finding to its operational or financial impact, and explain that connection in plain language to product, revenue cycle and clinical stakeholders.
Demonstrated ability to present findings to non-technical, client-facing, or executive audiences (not just internal PM stakeholders)
Comfort working with EHR, PM System or patient-scheduling data, and familiarity with payer/provider dynamics such as eligibility, claims and denials.
5+ years of experience in a data analyst, business analyst or product analyst role, ideally supporting a product or SaaS organization.
Strong SQL skills and comfort working directly with relational databases or a data warehouse.
Experience building dashboards and reports in a BI/visualization tool (e.g., Power BI, Tableau or Looker) or in spreadsheet-based tooling at scale.
Working knowledge of data structures, indexing, querying and data retrieval concepts.
Ability to turn a loosely defined business question into a structured analysis and communicate the findings clearly to non-technical stakeholders.
Ability to translate and present CRM data; Salesforce reporting experience preferred.

Nice To Haves

HIPAA training, certification or hands-on experience handling sensitive patient data in a regulated environment.
Experience in Big Data tools and languages (Python, R, Go or Spark) for data cleaning and analysis at scale.
Exposure to product analytics concepts (adoption funnels, cohort analysis, activation/retention metrics) and to empowered product-team operating models.
Exposure to streaming and batch data pipeline tooling (Kafka, AWS Glue or similar) and to large-scale transactional datasets (millions of records per day).
Familiarity with AI/ML-driven analytics – evaluating model or rules-engine output (e.g., Drools), monitoring accuracy and drift, and translating AI performance into business metrics.
Experience supporting a platform migration (e.g., SQL Server to Snowflake, Power BI to Tableau) while maintaining reporting continuity.

Responsibilities

Analyze insurance eligibility and coverage data across the scheduling, pre-registration and visit stages of the patient journey to track active coverage rate, plan identification accuracy and eligibility flag clearance.
Monitor accuracy and performance of AI-driven eligibility checks (e.g., Code 42 non-response, flag resolution), and surface trends by payer, location and client.
Partner with the Eligibility AI product manager to define and track success metrics before development begins, and report on outcomes (clean-claim impact, staff time saved, denial reduction) after launch.
Support revenue and opportunity modeling for initiatives, including attach-rate assumptions, unit economics and renewal risk analysis.
Pull and validate Salesforce data (strategic renewals, new-logo run-rate) feeding the opportunity model, flagging data-mapping issues before they reach leadership reporting.
Help quantify and track initiative adoption and revenue contribution against goals set by the product and revenue teams.
Build and maintain recurring client-facing and internal outcome reporting (usage, adoption, satisfaction, business impact) across product areas, in the spirit of the outcome-measurement goals every PM on this team is held to.
Consolidate exports from the Clearwave Provider Portal and other internal sources into consistent, reusable data models so reporting doesn't have to be rebuilt from scratch each cycle.
Help Product Managers document ROI for completed initiatives (e.g., Payment at Pre-Reg, Voice AI, Reschedule Assist) using adoption rates, transaction/revenue volume and customer satisfaction data.
Occasionally present outcome data directly in client business reviews or renewal conversations, alongside CS/Sales.
Build and maintain dashboards and self-serve reporting that let Product Managers track their product area's P&L, feature usage and client adoption without a one-off analysis request each time.
Translate ambiguous business questions from Product, CS and leadership into clean data pulls, clear visualizations and a defensible recommendation — grounded in how the finding affects patients, staff workflow or revenue, not just the numbers themselves.
Maintain data quality and documentation for the metrics and definitions this team relies on, so numbers stay consistent across PMs and reporting cycles.
Participate in discovery and prioritization conversations to make sure decisions are grounded in usage and outcome data, not just anecdote.