Product Manager

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 60 health plans, including many of the top 20, and representing more than 160 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.

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.

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.

• 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
  

You will partner with data engineering, data science, and platform teams on initiatives such as:

Data consolidation and unification across legacy platforms—working with engineering teams to coordinate migrations to unified infrastructure while maintaining production stability and enabling parallel product development

Canonical data model development—collaborating with data engineering and data science leadership to define product requirements for production-ready models covering medical claims, pharmacy claims, eligibility, and other core healthcare entities

Platform modernization—contributing product perspective to technical evaluations and roadmaps for lakehouse adoption, OLAP/OLTP separation, real-time processing capabilities, and distributed architecture patterns

AI-powered automation—partnering with technical teams to evaluate and implement LLM-based approaches that accelerate ETL development, data transformation, and migration workflows

Data discovery and cataloging—working with engineering to define requirements for capabilities that help teams understand what data exists, where it lives, how to access it, and what it means

Product team enablement—ensuring downstream product teams can successfully consume data platform capabilities through clear interfaces, comprehensive documentation, and responsive support

These represent current focus areas but the role will evolve based on business priorities, strategic direction, and technical roadmap.

• 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

Current State Mastery: Exceptional ability to rapidly understand complex legacy systems—navigating five different platforms with different data models, ETL patterns, and team cultures to discover how things actually work

Future State Vision: Can envision target architectures that will scale 10-100x beyond current state, articulate why they matter, and define pragmatic paths to get there

Execution Velocity: Move with extraordinary speed from analysis to decision to implementation. Bias toward shipping 80% solutions today over 95% solutions next quarter. Understand that speed is a competitive advantage.

AI-Augmented Productivity: Active, sophisticated use of AI tools (ChatGPT, Claude, Copilot, etc.) to accelerate analysis, generate SQL, synthesize information, draft documentation, and make faster decisions than traditional approaches

Technical Credibility: Data engineering and data science teams respect you because you can engage substantively in architectural discussions, understand their constraints, and spot issues before they become problems

Cross-Boundary Navigation: Excel at finding critical information across disparate systems and tribal knowledge; build trust across teams with different cultures and priorities; serve as connective tissue in high-pressure environments

Systems Thinking: Understand second and third-order effects of architectural decisions across platform, products, and operations

• 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
  

Transformational Impact: Your work enables product teams serving 160M+ lives to ship faster and unlocks significant business value across coordination of benefits, subrogation, audit, and payment integrity

Technical Depth: Engage substantively in cutting-edge architecture decisions—lakehouse formats, distributed systems, real-time processing, LLM-powered automation—not just coordinate meetings

Unique Challenge: Navigate five legacy platforms with different data models, technologies, and cultures—partnering with technical leadership to shape unified future state in a once-in-career data consolidation opportunity

Execution Autonomy: We value speed over process. Fast decision-making and shipping results matter more than perfect planning

AI-First Environment: We use Claude, LLMs, and AI-powered tools extensively throughout the organization. You're expected to leverage AI to move faster than traditional approaches

Strategic Partnership: Work alongside VP Data Engineering, CTO, and architecture leadership to shape data platform serving dozens of product teams and supporting a $5B healthcare intelligence platform

Reporting Structure: Sr. Director of Product Management, Platform & Data

Key Partnerships:

• VP of Data Engineering and CTO (primary technical partners for strategy and architecture)

• Data Engineering leadership (Eric, Andre, Sam)

• Data Science teams

• Platform Engineering teams

• Product teams across COB, Subrogation, Audit, Pharmacy PI, Complex Claims

• Architecture and Technical Strategy leadership

You will work in close partnership with the VP of Data Engineering and CTO, bringing the product lens to technical strategy discussions while they own engineering execution and technical architecture. Your role is to translate business needs into product requirements, coordinate across teams, and ensure data initiatives deliver value to product organizations.

Culture:

• Fast-paced with bias toward action over analysis paralysis

• Technically deep—we respect engineering expertise and engage substantively

• Lightweight processes that accelerate rather than burden

• Remote-first with occasional travel for strategic planning (~quarterly)

• Sophisticated use of AI tools to augment productivity expected and encouraged

Technical Environment:

• Current: Postgres (Citus), Apache Spark, AWS (S3, DMS, RDS), Python, SQL

• Evolving: Lakehouse formats (Delta Lake/Iceberg), streaming capabilities, unified data architecture, LLM-powered automation

• Scale: Hundreds of millions of records, petabyte-range data, multi-tenant architecture, dozens of data sources

Equal Employment Opportunity at Machinify

Machinify is committed to hiring talented and qualified individuals with diverse backgrounds for all of its positions. Machinify believes that the gathering and celebration of unique backgrounds, qualities, and cultures enriches the workplace. 

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