Technical Data Product Manager

Overview

Machinify is a leading healthcare intelligence company with expertise across the payment continuum, delivering value, transparency, and efficiency to health plan clients. We are building a configurable, AI-powered platform and serving over 60 health plans, representing more than 160 million lives.

Senior Technical Data Product Manager — Remote (United States) | Full-Time

Machinify is formed through the combination of five payment integrity industry leaders and is backed by New Mountain Capital. We integrate technology, clinical expertise, and data assets to deliver value across the payment continuum.

We are hiring a Senior Technical Data Product Manager to drive the data product roadmap in partnership with data engineering and architecture leadership. You will translate business needs into product requirements and coordinate execution across teams, reporting to the Sr. Director of Product Management for Platform & Data.

This role requires capability in three areas: deeply understanding complex current state, envisioning and defining future state, and executing rapidly to bridge them. You will assess the landscape, build credibility across technical teams, and ship measurable value quickly—expect clear product direction within 90 days and measurable impact on velocity shortly after.

You will partner with data engineering, data science, and platform engineering leadership to define and deliver data products and capabilities that enable product teams across coordination of benefits, subrogation, audit, pharmacy payment integrity, and complex claims solutions. You will connect business requirements to technical execution, consolidating disparate legacy systems into modern, unified infrastructure while enabling new product capabilities.

This is a hands-on technical role requiring prior experience as a data engineer, data scientist, or analytics engineer
. You must be comfortable writing SQL, reviewing data architectures, and engaging in technical discussions. You will work across multiple legacy platforms with different technologies, cultures, and tribal knowledge, requiring the ability to influence without direct authority.

• Assess current state rapidly: Work with data engineering and architecture teams to understand complex legacy landscapes—what exists, where critical information lives, and how systems actually work
• Contribute to future state vision: Shape target data architectures, canonical models, and platform capabilities to scale for product teams
• Develop product roadmap: Translate business priorities into data product requirements and sequence initiatives across migration, new capabilities, and product enablement
• Support technical evaluations: Contribute product perspective to make or buy decisions, technology evaluations (lakehouse formats, real-time processing, AI-powered automation), and architectural choices
• Define and track success metrics: Establish product-level OKRs, track adoption, and communicate progress to stakeholders

• Drive cross-functional delivery: Coordinate data initiatives from requirements to production with data engineering, data science, platform engineering, and product teams
• Unblock relentlessly: Identify and resolve dependencies, bottlenecks, and blockers
• Navigate complexity: Find critical information across legacy platforms and undocumented systems; synthesize insights and create clarity
• Facilitate decisions: Build consensus across teams with competing priorities
• Leverage AI extensively: Use AI tools to accelerate analysis, documentation, SQL generation, and decision-making
• Establish lightweight visibility: Create metrics, dashboards, and reporting with minimal overhead

• Partner with technical leadership: Work with data engineering, data science, and architecture leads while respecting technical ownership
• Translate requirements: Convert product needs into clear technical requirements for engineering
• Enable product teams: Ensure downstream teams can consume data platform capabilities via clear interfaces,…