Machinifyinc19d ago

Technical Data Product Manager

United Statesmid

Product ManagementTechnical Product ManagerData Product Manager

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Quick Summary

Key Responsibilities

Work with data engineering and architecture teams to understand complex legacy landscapes—what exists, where critical information lives,

Requirements Summary

deeply understanding complex current state , envisioning and defining compelling future state , and executing at extraordinary velocity to bridge the two.

Technical Tools

Product ManagementTechnical Product ManagerData 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 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.

Responsibilities

~1 min read

→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.

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

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