Product Manager | Technical Data

Machinify

1d•Remote

About The Position

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. Product Manager | Technical Data About Machinify Machinify is a leading healthcare intelligence company formed through the combination of five payment integrity industry leaders: The Rawlings Group, Apixio Payment Integrity, VARIS, Machinify's AI platform, and Performant Healthcare. Backed by New Mountain Capital and valued at approximately $5 billion, we serve over 60 health plans including many of the top 20, representing more than 160 million lives. We are building a unified AI-powered platform that transforms healthcare payments by combining revolutionary technology, clinical expertise, and rich data assets to deliver unmatched value, transparency, and efficiency across the payment continuum. Position Summary We are hiring a Senior Technical Data Product Manager to drive the data product roadmap in close partnership with data engineering and architecture leadership. Reporting to the Sr. Director of Product Management for Platform & Data, you will work alongside the VP of Data Engineering, CTO, and technical leads to translate business needs into product requirements and coordinate execution across teams. This role requires someone exceptional at three critical capabilities: deeply understanding complex current state, envisioning and defining compelling future state, and executing at extraordinary velocity to bridge the two. The right candidate will rapidly assess the landscape, build credibility across technical teams, and ship measurable value quickly—we expect clear thinking on product direction within the first 90 days and demonstrable impact on team 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 serve as the connective tissue between business requirements and technical execution, coordinating the consolidation of 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 substantively in technical discussions. You will work across multiple legacy platforms, each with different technologies, cultures, and tribal knowledge—requiring exceptional ability to influence without direct authority.

Requirements

10+ years total professional experience
5+ years in product management roles
Prior hands-on experience as data engineer, data scientist, or analytics engineer (required)
Proven track record shipping data products or platforms used by internal/external teams
Experience driving execution in matrixed organizations without direct authority
Demonstrated ability to assess complex technical landscapes and define future-state architectures
Data architecture expertise: Deep understanding of data modeling, normalization/denormalization, distributed systems, batch/streaming patterns, ETL/ELT design
Advanced SQL proficiency: Write complex queries, optimize performance, understand CDC patterns, validate data quality
Cloud data infrastructure: AWS preferred (S3, Spark, RDS, DMS, Glue) or equivalent GCP/Azure experience
Modern data stack fluency: Knowledge of data warehouses, lakehouse formats, orchestration tools (Airflow), transformation frameworks (DBT), BI platforms
Analytical rigor: Define metrics, analyze data, make data-driven decisions, identify patterns across complex systems
Strong stakeholder management across technical teams (data engineering, data science, platform) and business audiences
Ability to translate complex technical architectures into business outcomes and vice versa
Experience defining product vision, building roadmaps, and measuring success
Proven influence without direct authority—building consensus through credibility and data-driven arguments
Excellent written and verbal communication across all organizational levels
Agile/Scrum methodology experience

Nice To Haves

Experience with LLM/AI applications for data transformation, code generation, or workflow automation
Python proficiency for data analysis, prototyping, or understanding engineering implementations
Prior data platform migrations or consolidations at significant scale
Healthcare payment integrity, payer operations, or regulated industry experience
Hands-on experience with Snowflake, Databricks, Kafka, Fivetran, or similar modern data platforms
Background in distributed systems, database internals, or data-intensive applications
Fast-paced startup or high-growth company experience

Responsibilities

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: Partner with VP Data Engineering, CTO, and architecture leads to shape target data architectures, canonical models, and platform capabilities that will scale to support product teams
Develop product roadmap: Translate business priorities into data product requirements, working with technical leadership to sequence initiatives and balance migration work, new capabilities, and product enablement
Support technical evaluations: Contribute product perspective to build vs. 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 across product teams, and communicate progress to stakeholders
Drive cross-functional delivery: Coordinate data initiatives from requirements through production, working across data engineering, data science, platform engineering, and product teams
Unblock relentlessly: Identify and resolve dependencies, bottlenecks, and blockers before they slow down team velocity
Navigate complexity: Find critical information scattered across legacy platforms, undocumented systems, and tribal knowledge; synthesize insights and create clarity
Facilitate decisions: Build consensus across teams with competing priorities and different technical opinions
Leverage AI extensively: Use LLMs and AI-powered tools to accelerate analysis, documentation, SQL generation, information synthesis, and decision-making
Establish lightweight visibility: Create metrics, dashboards, and reporting that provide insight without creating overhead
Partner with technical leadership: Work closely with data engineering, data science, and architecture leads—contributing product perspective while respecting their technical expertise and domain ownership
Translate requirements: Convert product team needs into clear technical requirements that engineering teams can execute against
Enable product teams: Ensure downstream product teams can successfully consume data platform capabilities through clear interfaces, documentation, and support
Participate in technical discussions: Engage substantively in reviews of ETL pipelines, data models, distributed architectures, and platform decisions
Bridge stakeholders: Translate complex technical concepts into business value for executives and product teams; bring business context to technical discussions