Senior Product Manager, AI Data Foundation

ZoomInfo Technologies•Waltham, MA

1d•Hybrid

About The Position

ZoomInfo is where careers accelerate. We move fast, think boldly, and empower you to do the best work of your life. You’ll be surrounded by teammates who care deeply, challenge each other, and celebrate wins. With tools that amplify your impact and a culture that backs your ambition, you won’t just contribute. You’ll make things happen–fast. ZoomInfo is seeking a Sr Product Manager, AI Data Foundation to own our data quality monitoring platform, self-service reporting capabilities, and the internal tooling that powers data research and analysis across the organization. In this role, you'll work at the intersection of AI, data quality, and platform product management - building systems that make ZoomInfo's data teams faster, smarter, and more proactive. The Sr PM, AI Data Foundation will be an evangelist and builder, not a reactive order-taker. You'll see across the full data portfolio, identify opportunities for extreme leverage, and drive AI-enabled solutions for data quality enhancement and self-remediation. This role is critical to ZoomInfo's ability to scale data quality at speed and empower teams with the tooling they need to move faster.

Requirements

5+ years of product management experience with a focus on data products, platforms, internal tooling, or data-intensive B2B SaaS
Demonstrated experience building data quality, monitoring, observability, or self-service analytics products
Proven ability to identify and ship AI/ML-powered product capabilities, particularly applied to data quality, automation, or workflow tooling
Strong understanding of data quality frameworks, metrics, anomaly detection, and the systems that produce and monitor data at scale
Platform and tooling product mindset with experience driving adoption across diverse internal user bases
Strong analytical skills with ability to independently define metrics, analyze data, and make data-driven decisions
Ability to solve complex technical problems in partnership with engineering and data science teams
Excellent communication skills with ability to translate complex technical concepts for diverse audiences and write clear PRDs
Proactive orientation - demonstrated ability to identify leverage opportunities across a portfolio, not just respond to inbound requests
B2B SaaS experience, preferably in data platforms, developer tools, or data operations

Nice To Haves

Experience with data observability, data catalog, or data governance platforms
Background in data engineering, analytics engineering, or data science prior to product management
Familiarity with modern data stack tooling (dbt, Snowflake, Databricks, Fivetran, etc.)
Experience building AI-powered automation or self-remediation capabilities
Knowledge of data privacy regulations (GDPR, CCPA) and their product implications

Responsibilities

Own end-to-end product strategy for data quality monitoring, self-service reporting, and the internal Data Tools platform
Proactively identify opportunities across the data portfolio where tooling and automation can create extreme leverage for data teams, PMs, and analysts
Define and standardize metrics and KPIs for data quality, coverage, accuracy, and freshness across all data domains
Build and deliver self-service dashboards and reporting tools that give stakeholders real-time visibility into data health
Identify, prioritize, and ship AI-driven data quality enhancements and auto-remediation capabilities that reduce manual intervention at scale
Own the internal Data Tools roadmap, delivering tooling that meaningfully improves productivity for data researchers, analysts, and product managers
Partner with engineering and data science to develop AI-powered solutions for anomaly detection, root cause analysis, and self-healing data pipelines
Drive adoption of new tooling across data operations, engineering, and PM teams through evangelism, documentation, and enablement
Manage dependencies across data domains to ensure quality monitoring integrates seamlessly with upstream and downstream systems
Conduct discovery with internal stakeholders to uncover true tooling and workflow pain points beyond surface-level feedback