Senior Product Manager, Data - CertifyOS

About CertifyOS

CertifyOS is building the data infrastructure that powers modern healthcare.

Today, healthcare organizations rely on fragmented and outdated provider data. This creates unnecessary administrative work, regulatory risk, and higher costs across the system. We’re solving that problem.

Our API-first platform automates provider licensing, enrollment, credentialing, and network monitoring by connecting directly to hundreds of primary data sources. We help healthcare organizations maintain accurate, compliant, and reliable provider networks at scale.

Our vision is simple: One API. One provider ID. Frictionless provider data.

We’re backed by leading investors and built by a team with deep experience in provider data systems. At CertifyOS, we value authenticity, accountability, collaboration, results, and openness to feedback. We’re building a high-ownership team focused on solving real infrastructure problems that impact millions of patients.

About the Role

CertifyOS is building the data infrastructure for the next generation of provider technology. As we scale across Credentialing, Monitoring, Rosters, Provider Network Operations, the biggest bottleneck is no longer just building features - it is making the platform easier for internal teams and customers to configure, operate, and extend safely.

As the Senior Technical Product Manager, Data, you will own the strategy and execution for the system that keeps credentialed practitioners continuously compliant with NCQA standards. You'll be accountable for the end-to-end data provisioning experience, the data elements, the quality of that data, and the source veracity that proves historical compliance during audits. The ideal candidate would be comfortable making all provider data pipelines trustworthy, fast, configurable, and cheap to run at scale, while moving the team from a service-driven, manual-review posture toward a productized, self-serve, AI-native one.

This role sits at the intersection of platform thinking and technical product expertise. You'll partner closely with the Application Product team, Operations, Implementation and Client Success teams.

What You Will Do

Sourcing: Getting the Data

• Detect need & source routing: Define what triggers a data request (a new file, a monthly monitoring cycle, an expiring element) and the rules that route it to the best source: bulk file, API, scraper, paid vendor, outreach, or human analyst. Every attempt and its raw response is recorded.
• Source economics: Know the cost, coverage, and latency of every source. Decide when to build a scraper, buy an API, or pay for a bulk feed.

Association: Getting It on the Right Practitioner

• Attribution & matching: Define how acquired records are matched to practitioners across identifiers that are often missing or conflicting, including probabilistic matching models. Set the quality bar for false and missed matches, and own the review path for low-confidence results.
• Survivorship & MDM governance: Define which source wins for each attribute, and make that configurable. Build golden record rules, merge and unmerge capabilities, audit trails, and monitoring/observability loops so matching changes never ship blind.

Across the Chain

• Build explainability: Ensure anyone in the company can ask why a license is missing, stale, or attached to the wrong practitioner, and get a traced answer from source to screen. Partner with engineering on observability.
• Measure data loss end to end: Build the attribute-level funnel that shows where data drops out between acquisition and the application, and give each application pod a data health metric tied to its own north star.
• Keep data pipelines stable: Triage pipeline and data escalations, separate one-off fixes from product gaps, and cut the volume of manual re-scrapes and stale-data tickets.
• Define data platform metrics: Track metrics by application and client, source success rate and freshness, % of requests resolved without a human, match precision and recall, % of data errors caught before a client reports them, time to explain a data issue, and external data cost per practitioner.

Must-Haves

• AI-Native Execution: Highly productive with tools like Claude Code, Codex, Cursor, or similar to query data, prototype, write specs, and automate your workflow.
• Experience: 5 to 8 years of total experience, with at least 3 years in technical product management focused on data products or backend systems.
• Technical Fluency: Strong background across APIs, data pipelines, retries and orchestration, and third-party data integrations.
• Data Domain Expertise: Hands-on experience with data acquisition/scraping/ingestion or entity resolution, record linkage, MDM, or identity matching.
• SQL & Data Literacy: Strong SQL skills, data completeness/freshness metrics, data modeling basics, and data warehouse tooling (BigQuery preferred).
• Workflow Automation: Proven track record of turning manual operational workflows into product capabilities.
• Clear Communication: Ability to write clear, structured specifications that engineers and coding agents can execute against.

Nice-to-Haves

• Experience in healthcare technology, provider data management, credentialing, or NCQA/CMS-regulated data environments.
• Experience with rules/flagging engines, exception-review workflows, or compliance audit tooling.
• Experience building AI-assisted internal tools, agentic triage/resolution, or support copilots.
• Familiarity with BigQuery, embedded analytics, or customer-facing compliance reporting.
• Background as an engineer or technical implementation lead prior to product management.
• Startup or high-growth experience in data platforms or US SaaS applications.

Benefits

• 100% coverage of health, dental, and vision insurance premiums.
• Unlimited PTO (US) / statutory leave and wellness leave (India).
• Comprehensive health and wellness benefits.