Senior Product Manager, Data

Overview

Own the strategy and execution for the system that keeps credentialed practitioners continuously compliant with NCQA standards, covering the end-to-end data provisioning experience, data elements, data quality, and source veracity.

What you'll do

• Own "detect need" and 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 a human analyst.
• Record every attempt and its raw response.
• Own source economics: know the cost, coverage and latency of every source, and decide when to build a scraper, buy an API or pay for a bulk feed.
• Own attribution and 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.
• Own survivorship and Master Data Management governance: define which source wins for each attribute, make that configurable, and own golden record rules, merge and unmerge, audit trail, and monitoring and observability loops so matching changes never ship blind.
• Build explainability so 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, partnering with engineering on the observability.
• Measure data loss end to end with an attribute-level funnel from acquisition to 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 metrics for data platform effectiveness: 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.
• Turn manual operational workflows into product capability, and say no to one-off requests.

What you'll need

• AI-native execution: at home and highly productive in Claude Code, Codex, Cursor or similar, using them to query data, prototype, write specs and automate your own work, and knowing where they fail.
• 5 to 8 years of experience, with at least 3 years in technical product management for data products or backend products.
• Technical fluency across APIs, data pipelines, retries and orchestration, and third-party data integrations.
• Hands-on experience with at least one of: data acquisition, scraping or ingestion; or entity resolution, record linkage, MDM or identity matching.
• Strong SQL and data literacy: completeness and freshness metrics, data modeling basics, warehouse tooling (BigQuery a plus); you query the data yourself before forming an opinion.
• Experience turning manual operational workflows into product capability, with the judgement to say no to one-off requests.
• Clear written communication: you write specs an engineer and a coding agent can both build from.

Nice to have

• 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 before PM.
• Startup or high-growth experience in data platform or US SaaS applications.

Details

• Location: Bengaluru, India.
• Partners closely with the Application Product, Operations, Implementation and Client Success teams.