Senior Product Manager, Data
Posted November 9, 2026
Job Description
- This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Product Manager, Data based in India. In this role, you will lead the strategy and development of a data platform that powers modern healthcare technology and enables reliable provider information at scale. You will own the end-to-end data provisioning experience, ensuring data accuracy, quality, traceability, and compliance with industry standards. Working at the intersection of technical product management, data infrastructure, and AI-driven automation, you will help transform manual, service-oriented processes into scalable, self-service product capabilities. You will drive improvements across data sourcing, ingestion, entity resolution, and master data management. Collaborating closely with engineering, operations, implementation, and client success teams, you will ensure that data pipelines remain trustworthy, efficient, and cost-effective. This is an opportunity to shape a critical healthcare infrastructure platform while delivering measurable operational and customer impact in a high-ownership, fast-growing environment.
- Accountabilities: Own data acquisition strategy: Define how data needs are detected and determine the most effective sources for fulfilling requests, including APIs, bulk files, web scraping, third-party vendors, direct outreach, and human review. Optimize data sourcing: Evaluate and manage the cost, coverage, reliability, and latency of data sources, making informed decisions about when to build, buy, or integrate external solutions. Lead data attribution and matching: Develop strategies for accurately associating acquired records with healthcare practitioners, including probabilistic matching models, identifier management, and confidence-based review workflows. Drive master data management: Establish governance rules for source prioritization, golden records, data merging and unmerging, audit trails, and configurable attribute-level data management. Improve data transparency and explainability: Ensure teams can trace data issues from the original source through to the application, working with engineering to implement robust monitoring and observability capabilities. Monitor end-to-end data quality: Build metrics and reporting that identify where data is lost, incomplete, or outdated throughout the acquisition and delivery process. Strengthen platform reliability: Manage data pipeline escalations, distinguish recurring product gaps from isolated issues, and reduce manual interventions, unnecessary re-scraping, and stale-data incidents. Define and track success metrics: Establish performance indicators covering source success rates, data freshness, matching precision and recall, automation rates, error detection, resolution times, and external data costs per practitioner. Promote AI-driven automation: Move the platform toward scalable, self-service, AI-native workflows that reduce operational complexity and improve efficiency across product and operations teams. Requirements: Professional experience: 5–8 years of overall professional experience, including at least 3 years in technical product management, preferably working on data products or backend platforms. Technical expertise: Strong understanding of APIs, data pipelines, orchestration, retries, and third-party data integrations. Data acquisition or management experience: Hands-on experience in at least one of the following areas: data acquisition, web scraping, data ingestion, entity resolution, record linkage, master data management, or identity matching. SQL and data analytics: Strong SQL skills and data literacy, including experience with data completeness, freshness metrics, data modeling fundamentals, and warehouse tools. Familiarity with BigQuery is an advantage. AI-native mindset: Highly comfortable using AI-powered development tools such as Claude Code, Codex, Cursor, or similar platforms to query data, prototype solutions, write specifications, and automate workflows, while understanding their limitations. Product execution: Proven ability to transform manual operational processes into scalable product capabilities and prioritize long-term solutions over isolated, one-off requests. Communication skills: Excellent written communication skills, with the ability to create clear, actionable product specifications that both engineers and AI coding agents can effectively implement. Healthcare industry knowledge: Experience in healthcare technology, provider data management, credentialing, or regulated environments involving NCQA or CMS standards is a strong advantage. Workflow and compliance expertise: Familiarity with rules engines, flagging systems, exception-review workflows, or compliance audit tools is desirable. AI product experience: Experience developing AI-assisted internal tools, agentic triage and resolution workflows, or support copilots is a plus. Additional technical knowledge: Familiarity with embedded analytics, customer-facing compliance reporting, and BigQuery is beneficial. Relevant background: Previous experience as a software engineer or technical implementation lead before transitioning into product management is an advantage. Working environment: Experience in startups or high-growth SaaS companies, particularly in data platforms or applications serving the US market, is preferred. Benefits: Health coverage: Health insurance and statutory employee benefits in India. Additional wellness support: Menstrual leave for women, alongside other wellness benefits. Work flexibility: Fully remote working arrangement. Work-life balance: Statutory leave benefits and additional time off to support employee well-being. Inclusive workplace: An equal opportunity environment that values diversity, authenticity, accountability, collaboration, and openness to feedback. Transparent compensation: A commitment to pay transparency and an open culture that encourages respectful compensation discussions. Career development: The opportunity to shape innovative data infrastructure and contribute to AI-powered solutions in a growing healthcare technology environment.
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