Senior Product Manager, Reporting & Analytics (Remote -US)

Why join Nextech?

We are a leader in specialty healthcare technology solutions.

We’re committed to hiring and retaining talent, which is why we invest in our employees through competitive pay, a generous bonus structure, great healthcare, a comprehensive wellness program, and many other benefits.

If you are a software engineer, finance or accounting professional, customer support specialist, or a business development expert with a passion for healthcare technology (just to name a few), we want to hear from you.

We are an equal opportunity employer with a commitment to diversity.  All individuals, regardless of personal characteristics are encouraged to apply. If you are a candidate in need of assistance or an accommodation in the application process, please contact talent@nextech.com.

Essential Functions: In addition to working in accordance with appropriate conduct and behavioral standards, specific responsibilities of this role include:

• Product Vision and Strategy
• Own the product vision, strategy, and multi-quarter roadmap for Nextech's reporting, analytics, and AI-driven insights portfolio across financial and clinical domains.
• Set direction in ambiguity by making clear, defensible bets on where to invest, what to sequence, and what to decline, grounded in client value and business impact.
• Define the analytics and insights strategy that positions Nextech ahead of client and market needs, including the shift toward proactive, AI-driven insight.
• Prioritize and manage the portfolio backlog, making data-driven tradeoffs on scope, sequencing, and risk.
• Ensure responsible and compliant use of AI and client data aligned with regulatory and company standards.
• Modern Data Architecture and Analytics Platform

Work closely with Data Engineering to understand how data is constructed across the lakehouse's medallion layers, moving from raw ingestion (bronze) to cleansed and conformed data (silver) to curated, analytics-ready data products (gold), then translate that understanding into sound product decisions and tradeoffs.

Help shape the broader data foundation that powers reporting and AI-driven insights, including the data warehouse and data marts, ELT/ETL pipelines, semantic and metrics layers, and embedded analytics.

Translate a modern data architecture vision into a product roadmap that scales across specialties and clients.

Make informed tradeoffs on data modeling, latency, governance, and reusability that balance speed to value against long-term platform health.

Champion data quality, lineage, and trust as first-class product outcomes, since analytics is only as valuable as the data beneath it.

Discovery and Client Insight

Drive continuous, structured discovery with providers, practice administrators, revenue cycle leaders, and clinical staff to validate needs, financial and clinical use cases, and willingness to pay.

Run discovery at pace, converting client learning into clear, actionable decisions the delivery team can execute quickly, rather than open-ended research.

Synthesize qualitative and quantitative signal into a sharp, defensible point of view on what to build and why.

Validate direction early and often through prototypes, pilots, and design partners to reduce delivery risk.

Requirements Definition and Engineering Partnership

• Author clear, crisp, and unambiguous product requirements (outcomes, epics, user stories, acceptance criteria, and workflow documentation) that engineering and design can act on without guesswork.
• Demonstrate extreme ownership in driving product refinement alongside engineering counterparts, staying in the details from definition through delivery.
• Partner deeply with engineering on technical tradeoffs, data dependencies, and sequencing, resolving ambiguity before it becomes rework.
• Maintain a healthy, well-groomed backlog that keeps the delivery team moving at a predictable pace.
• Execution, Delivery, and Team Leadership
• Lead cross-functional execution to deliver high-quality reporting and analytics capabilities in iterative, value-driven releases.
• Galvanize engineering and design teams around a shared vision, creating the clarity, momentum, and urgency that drive fast, quality delivery.
• Operate as a force multiplier by removing blockers, managing dependencies, and raising the bar on craft across the teams you influence.
• Model and mentor strong product practices, elevating the discovery, definition, and delivery discipline of the broader product team.
• Metrics, Business Impact, and Commercial Success
• Define, track, and report on the financial and clinical KPIs that matter in non-acute healthcare, including revenue cycle performance, charge capture and charge lag, accounts receivable aging, denials and net collection rate, underpayment detection, and clinical quality and operational measures.
• Tie product outcomes to measurable client and business impact, including adoption, retention, revenue, and efficiency gains.
• Partner with Commercial teams on positioning, packaging, launch, and adoption strategies for reporting and analytics offerings.
• Use data and market insight to guide prioritization and continuously assess product-market fit.
• Additional duties as required.
• Minimum Requirements: 6+ years of product management experience, including significant time owning reporting, analytics, or data-driven products.
• Demonstrated fluency in financial outcomes and KPIs in non-acute, ambulatory, or specialty healthcare, including revenue cycle management, charge capture, accounts receivable aging, denials, net collection rate, and underpayment detection.
• Proven track record of delivering measurable results in fast-paced, high-growth environments.
• Working knowledge of modern data architecture, including the data warehouse and data marts, ELT/ETL pipelines, semantic and metrics layers, and embedded business intelligence, and how it enables analytics and AI-driven insights.
• Conversant in data lake and lakehouse architecture and the medallion model of refining data from raw ingestion (bronze) to cleansed, conformed data (silver) to curated, analytics-ready products (gold). Must understand how the data is constructed well enough to partner with engineering on design tradeoffs. This is a step beyond domain or subject-matter expertise, though it does not require hands-on data engineering.