Data Product Manager

Globality•Palo Alto, CA

1d•Onsite

About The Position

Globality is hiring a Data Product Manager to own the strategy for capturing, structuring, governing, and using data generated through its autonomous sourcing platform. The role will turn data on purchase intent, supplier pricing and performance, negotiation outcomes, and award decisions into a proprietary asset that improves the platform’s agents and customer outcomes. The Data Product Manager will also own analytics and benchmark products that help customers understand sourcing results. The position partners with Engineering, Machine Learning, the AI Product Manager, Product leadership, Legal, Security, and Sales. It is based in the Palo Alto office, with employees expected to work there four days a week. The anticipated annual base salary range is $150,000–$250,000, with actual compensation varying based on factors including experience, skills, and location.

Requirements

5+ years of product management experience, including 3+ years owning data platform, data infrastructure, or AI/ML data products.
Track record of turning proprietary data into a strategic asset, such as through data network effects, industry benchmarks, or training data that measurably improved model performance.
Deep understanding of data architecture, including data modeling, pipelines, warehouses and lakehouses, entity resolution, and master data management.
Experience working closely with ML teams on training data, labeling, evaluation datasets, and feedback loops for machine learning or LLM-based systems.
Experience structuring and normalizing messy, multi-source enterprise data.
Working knowledge of data rights, privacy, and governance in multi-tenant B2B SaaS, including anonymization and aggregation of customer data.
Strong technical fluency, including SQL, and ability to assess data quality and validate assumptions independently.
Strategic, business-oriented mindset and ability to articulate the economic value of data and make the case for investment to executive leadership.
Experience turning customer or operational data into analytics, dashboards, or benchmarks used by external customers.

Nice To Haves

Experience with procurement, sourcing, or spend data.

Responsibilities

Own the data product strategy and roadmap, defining how Globality captures, structures, and compounds customer and sourcing data into a proprietary asset.
Identify and prioritize data with durable competitive value, including purchase intent, should-cost benchmarks, supplier pricing and performance, negotiation outcomes, and award decisions.
Design instrumentation to capture high-value signals throughout the intake-to-award process and address gaps where data is lost or left unstructured.
Define canonical data models, taxonomies, and entity resolution for suppliers, categories, and commercial terms, unifying data across customers, ERP platforms, and procurement systems.
Build feedback loops that turn sourcing outcomes into training data, evaluation sets, and benchmarks to improve agents, including target pricing, supplier discovery, negotiation strategy, and award recommendations.
Partner with the AI Product Manager, Machine Learning, and Engineering to make data usable by models and agents, including labeled datasets, feature pipelines, and retrieval sources.
Define data-powered product capabilities, such as market pricing and should-cost benchmarks, and work with Product leadership to prioritize which to build.
Work with Legal, Security, and Sales to establish data rights, consent, and contractual frameworks for use of aggregated and anonymized data across customers.
Set data quality, coverage, and freshness standards and measure the data asset’s value through metrics such as category coverage, model performance lift, and customer outcomes.
Own the roadmap for customer-facing analytics, including spend and savings reporting and customer insight dashboards, in partnership with Product leadership.
Define consistent customer-result metrics, including cycle time, savings, spend under management, supplier participation, and award outcomes, and document them in a shared data dictionary.
Ensure customer data is handled in accordance with SOC 2 Type 2, ISO 27001:2022, GDPR, and CCPA requirements, including data segregation and access controls.