Senior Product Manager, Data

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Monitor end-to-end data quality: Build metrics and reporting that identify where data is lost, incomplete, or outdated throughout the acquisition and delivery process.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.