Senior Product Manager, Data
New
J
JobgetherHealthcare technology
Based in IndiaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5–8 years of overall professional experience, including at least 3 years in technical product management
- Required Skills
- SQLRESTful APIsBigQuery
Requirements
- Have 5–8 years of overall professional experience, including at least 3 years in technical product management.
- Bring strong understanding of APIs, data pipelines, orchestration, retries, and third-party data integrations.
- Have hands-on experience in at least one of data acquisition, web scraping, data ingestion, entity resolution, record linkage, master data management, or identity matching.
- Have strong SQL skills and data literacy, including data completeness and freshness metrics, data modeling fundamentals, and warehouse tools.
- Be comfortable using AI-powered development tools such as Claude Code, Codex, or Cursor to query data, prototype solutions, write specifications, and automate workflows.
- Be able to transform manual operational processes into scalable product capabilities and prioritize long-term solutions.
- Have excellent written communication skills and be able to create actionable product specifications for engineers and AI coding agents.
- Healthcare technology, provider data management, credentialing, or regulated-environment experience involving NCQA or CMS standards is advantageous.
- Familiarity with rules engines, flagging systems, exception-review workflows, or compliance audit tools is desirable.
- AI-assisted internal tools, agentic triage and resolution workflows, or support copilot experience is a plus.
- Familiarity with embedded analytics, customer-facing compliance reporting, and BigQuery is beneficial.
- Prior software engineering or technical implementation lead experience, and experience in startups or high-growth SaaS companies, are preferred.
Responsibilities
- Define the data acquisition strategy and determine appropriate sources, including APIs, bulk files, web scraping, vendors, direct outreach, and human review.
- Evaluate and manage data sources for cost, coverage, reliability, and latency, and decide when to build, buy, or integrate solutions.
- Develop strategies for matching acquired records to healthcare practitioners, including probabilistic matching, identifier management, and confidence-based review workflows.
- Establish master data management governance for source prioritization, golden records, data merging and unmerging, audit trails, and attribute-level management.
- Work with engineering to improve data traceability, monitoring, and observability.
- Build metrics and reporting to identify data that is lost, incomplete, or outdated across acquisition and delivery.
- Manage data pipeline escalations and reduce manual interventions, unnecessary re-scraping, and stale-data incidents.
- Define and track performance indicators for source success, freshness, matching, automation, error resolution, and external data costs.
- Promote scalable, self-service, AI-native workflows across product and operations teams.
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