Technical Product Manager, Provider Data

Helm Health

3d•$120,000 - $150,000•Remote

About The Position

At Helm, we are a Series A start-up transforming health insurance with "Dynamic Copay" – a new insurance plan that lets members see simple, upfront prices for all medical care before making decisions. Our team is building the infrastructure to power these plans for health insurance payors. With Helm, our clients offer simpler health plans to their members, helping them navigate to higher-value care. Our team has specialized in Dynamic Copay solutions since 2020, and Helm is the only independent platform in the market. We have grown rapidly since our launch, working with clients from local health plans to the nation's largest health insurers. The market is forming around us, making it an exciting time to join! We're seeking a Technical Product Manager to own our provider network data, from ingestion and normalization through analysis and productization. While this is a Product Manager role in the long term, it starts as a deeply hands-on analyst position. You’ll be expected to live in the data, understand its quirks, and turn messy inputs into reliable, scalable products. Think of this as a PM who can out-analyze most analysts.

Requirements

Strong analytical background with heavy SQL usage (this is non-negotiable)
Experience working with large, messy datasets
Comfort owning ambiguous problem spaces and creating structure
Ability to explain complex data concepts clearly to non-technical stakeholders
Interest flexing between analyst and PM and evolving into a full Product Manager role
Strong attention to detail with a bias toward action
Prior experience with provider data (NPI, TIN, specialties, directories, networks)
Background as a data analyst, analytics engineer, or similar
Experience defining data products or internal platforms

Responsibilities

Own the end-to-end provider network data domain:
Provider identities
Locations
Specialties
Network participation
Analyze incoming provider data to identify gaps, inconsistencies, and quality issues
Build a deep understanding of how provider data is sourced, transformed, and used
Partner with engineering to define data models, pipelines, and validation logic
Create requirements for data quality rules, enrichment, and deduplication
Answer complex questions using SQL and data analysis
Gradually transition from execution-heavy analysis to roadmap ownership and strategy
Define and track data quality KPIs (coverage, accuracy, freshness)